Risk Report - Risk Management

12 Months Ended

Dec. 31, 2022

Risk Management [Abstract]

Risk Identification and Assessment [text block]

Risk identification and assessment Risks to Deutsche Bank’s businesses and infrastructure functions, including under stressed conditions, are regularly identified. This assessment incorporates input from both 1st LoD and 2nd LoD, with the identified risks assessed for materiality based on their severity and likelihood of materialization. The assessment of risks is complemented by a view on emerging risks applying a forward-looking perspective. This risk identification and assessment process results in the risk inventory which captures the material risks for the Group, and where relevant, across businesses, entities and branches. Regular updates to the risk inventory are reported to senior management for review and challenge, and subsequently inform key risk management processes. These include the development of stress scenarios tailored to Deutsche Bank’s risk profile, and informing risk appetite setting and monitoring. Risks in the inventory are also mapped to risks in the risk type taxonomy.

Credit Risk Management [Abstract]

Credit Risk Framework [text block]

Credit risk framework Credit risk arises from all transactions where actual, contingent or potential claims against any counterparty, borrower, obligor or issuer (which Deutsche Bank refers to collectively as “counterparties”) exist, including those claims that Deutsche Bank plans to distribute; these transactions are typically part of the bank’s non-trading lending activities (such as loans and contingent liabilities) as well as the bank's direct trading activity with clients (such as OTC derivatives); these also include traded bonds and debt securities; carrying values of equity investments are also disclosed in the bank’s Credit Risk section. Deutsche Bank manages the respective positions within the bank’s market risk and credit risk frameworks. Based on the Risk Type Taxonomy, credit risk is grouped into four material categories, namely default / migration risk, transaction / settlement risk (exposure risk), mitigation risk and credit concentration risk. This is complemented by a regular risk identification and materiality assessment. – Default / migration risk as the main element of credit risk, is the risk that a counterparty defaults on its payment obligations or experiences material credit quality deterioration increasing the likelihood of a default – Transaction / settlement risk (exposure risk) is the risk that arises from any existing, contingent or potential future positive exposure – Mitigation risk is the risk of higher losses due to risk mitigation measures not performing as anticipated – Credit concentration risk is the risk of an adverse development in a specific single counterparty, country, industry or product leading to a disproportionate deterioration in the risk profile of Deutsche Bank’s credit exposures to that counterparty, country, industry or product Deutsche Bank manages its credit risk using the following philosophy and principles: – Credit Risk Management (CRM) forms part of the 2nd LoD within DB Group’s three Lines of Defense model. Business as primary risk taker and owner forms the 1st LoD and Group Audit the 3rd LoD – Compliance is reporting to a different Management Board Member and hence the credit risk function is independent from the compliance function up to Management Board level – In each of the bank’s divisions, credit decision standards, processes and principles are consistently applied – A key principle of credit risk management is client credit due diligence; Deutsche Bank’s client selection is achieved in collaboration with the bank’s business division counterparts who stand as a first line of defense – Deutsche Bank aims to prevent undue concentration and tail-risks (large, unexpected losses) by maintaining a diversified credit portfolio; client, industry, country and product-specific concentrations are assessed and managed against the bank’s risk appetite – Deutsche Bank maintains underwriting standards aiming to avoid large undue credit risk on a counterparty and portfolio level; in this regard Deutsche Bank extends also unsecured cash positions and actively use hedging for risk mitigation purposes; additionally, Deutsche Bank strives to secure its derivative portfolio through collateral agreements and may additionally hedge concentration risks to further mitigate credit risks from underlying market movements – Every new credit facility and every extension (such as exposure limit increase) to any counterparty requires credit approval at the appropriate authority level in line with the minimum required credit authority calculation within an established credit authority grid. Deutsche Bank assigns credit approval authorities to individuals according to their qualifications and experience, and Deutsche Bank reviews these periodically – Deutsche Bank manages all its credit exposures to each obligor across the bank’s consolidated Group on the basis of the “one obligor principle” (as required under Article 4(1)(39) CRR and related regulatory guidance), under which all facilities to a group of borrowers which are linked to each other (for example by one entity holding a majority of the voting rights or capital of another) are consolidated under one group – Deutsche Bank has established within Credit Risk Management – where appropriate – specialized teams for deriving internal client ratings, analyzing and approving transactions, monitoring the specific portfolios or covering workout clients; for transaction approval purposes, structured credit risk management teams are aligned to the respective lending business areas to ascertain adequate product expertise. – Where required, Deutsche Bank has established processes to manage credit exposures at a legal entity level – To meet the requirements of Article 190 CRR, DB Group has allocated the various control requirements for the credit processes to 2nd LoD units that are best suited to perform such controls

IFRS 9 Impairment [text block]

IFRS 9 Impairment

In the following chapter, the Group provides an overview of the IFRS 9 impairment framework and how it is embedded into Deutsche Bank‘s credit risk management activities. The first section provides a description of the Group‘s IFRS 9 model and methodology, along with the key model assumptions. In light of the current macroeconomic environment, the disclosure continues by highlighting key areas of focus in 2022 and how Deutsche Bank assessed the latest developments in its ECL calculation, and in particular, how the model properly took into account the impacts of the uncertainties noted in 2022 and at year end, along with the impact from reasonable changes in the Group’s key assumptions. These credit risk management activities are embedded in the bank’s overall control and governance framework for credit risk and governance over its ECL model. These activities include, but are not limited to, regular emerging risk reviews as well as portfolio deep dives, day to day risk management on the level of individual borrowers, as well as regular model validations. In the section Management overlays applied to the IFRS 9 model, the Group presents how it performed reviews of relevant assumptions and inputs to the ECL calculation, including the above key assumptions, and how as part of the model reviews, it assessed potential model imprecision and whether any corrective measures in the form of overlays was necessary. The Group also presents an overview and background on each of the management overlays recorded throughout 2022 and at year end. To provide additional transparency on the impact of reasonable changes to the key assumptions, the Group presents model sensitivities in a separate section, and concludes with the key drivers for the IFRS 9 model results.

Description of IFRS 9 Model and Methodology

The impairment requirements of IFRS 9 apply to all credit exposures that are measured at amortized cost or fair value through other comprehensive income and to off balance sheet lending commitments, such as loan commitments and financial guarantees. For purposes of the bank’s impairment approach, the Group refers to these instruments as financial assets.

The Group determines its allowance for credit losses in accordance with IFRS 9 as follows:

• – Stage 1 reflects financial assets where it is assumed that credit risk has not increased significantly after initial recognition
• – Stage 2 contains all financial assets, that are not defaulted, but have experienced a significant increase in credit risk since initial recognition
• – Stage 3 consists of financial assets which deemed to be in default in accordance with Deutsche Bank’s policies, which are based on the Capital Requirements Regulation (CRR) Article 178. The Group defines these financial assets as impaired, non-performing and defaulted
• – Significant increase in credit risk is determined using quantitative and qualitative information based on the Group’s historical experience, credit risk assessment and forward-looking information
• – Purchased or Originated Credit-Impaired (POCI) financial assets are assets where at the time of initial recognition there is objective evidence of impairment

The IFRS 9 impairment approach is an integral part of the Group’s credit risk management procedures. The estimation of expected credit losses (ECLs) is either performed via the automated, parameter based ECL calculation using the Group’s ECL model or determined by credit officers. In both cases, the calculation takes place for each financial asset individually. Similarly, the determination of the need to transfer between stages is made on an individual asset basis. The Group’s ECL model is used to calculate the allowance for credit losses for all financial assets in Stage 1 and Stage 2, as well as for Stage 3 in the homogeneous portfolio (i.e. retail and small business loans with similar credit risk characteristics). For financial assets in the bank’s non-homogeneous portfolio in Stage 3 and for POCI assets, the allowance for credit losses is determined individually by credit officers.

The Group uses three main components to measure ECL. These are Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD). The Group leverages existing parameters used for determination of capital demand under the Basel Internal Ratings Based Approach (IRBA) and internal risk management practices as much as possible to calculate ECL. These parameters are adjusted where necessary to comply with IFRS 9 requirements (e.g. use of point in time ratings and removal of downturn add-ons in the regulatory parameters). Incorporating forecasts of future economic variables into the measurement of ECL influences the allowance for credit losses in Stage 1 and 2. In order to calculate lifetime ECL, the Group’s calculation derives the corresponding lifetime PDs from migration matrices that reflect economic forecasts.

Stage Determination and Significant Increase in Credit Risk

At initial recognition, financial assets are reflected in Stage 1, unless the financial assets are POCI. If there is a significant increase in credit risk, the financial asset is transferred to Stage 2. A significant increase in credit risk is determined by using rating-related and process-related indicators. The assignment of financial assets to Stage 3 is based on the status of the borrower being in default. If a borrower is in default, then all financial assets of the borrower are transferred to Stage 3.

Rating-related Stage 2 indicators: The Group compares a borrower’s lifetime PD at the reporting date with lifetime PD expectations at the date of initial recognition to determine if there has been a significant change in the borrower’s PDs and consequently to any of the borrower’s transaction in the scope of IFRS 9 impairment. Based on historically observed migration behavior and a sampling of different economic scenarios, a lifetime PD distribution is obtained. A quantile of this distribution, which is defined for each counterparty class, is chosen as the lifetime PD threshold. If the remaining lifetime PD of a transaction according to current expectations exceeds this threshold, the financial asset has incurred a significant increase in credit risk and is transferred to Stage 2. The quantiles used to define Stage 2 thresholds are determined using expert judgment, are validated annually and have not changed since implementation of IFRS 9. The thresholds applied vary depending on the original credit quality of the borrower, elapsed lifetime, remaining lifetime and counterparty class. Management believes that the defined approach and quantiles represent a meaningful indicator that a financial asset has incurred a significant increase in credit risk.

Process-related Stage 2 indicators are derived via the use of existing risk management indicators, which in the bank’s view represent situations where the credit risk of financial assets has significantly increased. These include borrowers being added to a credit watchlist, being transferred to workout status, payments being 30 days or more past due or being in forbearance. As long as the conditions for one or more of the process-related or rating-related indicators is fulfilled and the borrower of the financial asset has not met the definition of default, the asset will remain in Stage 2. If the Stage 2 indicators are no longer fulfilled and the financial asset has not defaulted, the financial asset transfers back to Stage 1. In case of performing forborne financial assets, the probation period is 2 years before the financial asset is reclassified to Stage 1, which is aligned with regulatory guidance.

If the borrower defaults, all transactions of the borrower are allocated to Stage 3. If at a later date the borrower is no longer in default, the curing criteria according to regulatory guidance is applied (including probation periods), which are at least 3 months or 1 year in case of distressed restructurings. Once the regulatory cure period or criteria has been met, the borrower will cease to be classified as defaulted and will be transferred back to Stage 2 or Stage 1.

The ECL calculation for Stage 3 distinguishes between transactions in homogeneous and non-homogenous portfolios, and POCI financial assets. For transactions that are in Stage 3 and in a homogeneous portfolio, the Group uses a parameter based automated approach to determine the credit loss allowance per transaction. For these transactions, the LGD parameters are partially modelled to be time dependent, i.e. consider the declining recovery expectation as time elapses after default. The allowance for credit losses for financial assets in the bank’s non-homogeneous portfolios in Stage 3, as well as for POCI assets are determined by credit officers and have to be approved along an established authority grid up to and including the Management Board. This allows credit officers to consider currently available information and recovery expectations specific to the borrowers and the financial assets at the reporting date.

Estimation Techniques for Key Input Factors

The first key input factor in the Group ECL calculation is the one-year PD for borrowers which is derived from the bank’s internal rating systems. The Group assigns a PD to each borrower credit exposure based on a 21-grade master rating scale for all of the Group’s exposure.

The borrower ratings assigned are derived based on internally developed rating models which specify consistent and distinct customer-relevant criteria and assign a rating grade based on a specific set of criteria as given for a certain customer. The set of criteria is generated from information sets relevant for the respective customer segments including general customer behavior, financial and external data (e.g. credit bureau). The methods in use range from statistical scoring models to expert-based models taking into account the relevant available quantitative and qualitative information. Expert-based models are usually applied for borrowers in the exposure classes “Central governments and central banks”, “Institutions” and “Corporates” with the exception of those “Corporates” for which a sufficient data basis is available for statistical scoring models. For the latter as well as for the retail segment statistical scoring or hybrid models combining both approaches are commonly used. Quantitative rating methodologies are developed based on applicable statistical modelling techniques, such as logistic regression.

One-year PDs are extended to multi-year PD curves using through-the-cycle matrices and macroeconomic forecasts. Based on economic scenarios centered around the macroeconomic baseline forecast, through-the-cycle matrices are first transformed into point-in-time rating migration matrices, typically for a two-year period. The calculation of the point-in-time matrices leverages a link between macroeconomic variables and the default and rating behavior of borrowers, which is derived from historical macroeconomic variables (MEVs) and rating time series through regression techniques. In a final step, multi-year PD curves are derived from point-in-time rating migration matrices for periods where reasonable and supportable forecasts are available and extrapolated based on through-the-cycle rating migration matrices beyond those periods.

The second key input into the ECL calculation is the LGD parameter, which is defined as the likely loss intensity in case of a borrower’s default. It provides an estimation of the exposure that cannot be recovered in a default event and therefore captures the severity of a loss. Conceptually, LGD estimates are independent of a borrower’s probability of default. The LGD models applied in stages 1 and 2, which are based on regulatory LGD models, but adjusted for IFRS 9 requirements (i.e. removal of downturn-add-on and removal of indirect costs of workout), ensure that the main drivers for losses (i.e. different levels and quality of collateralization and customer or product types or seniority of facility) are reflected as risk drivers in LGD estimates. In the bank’s LGD models, the Group assigns collateral type specific LGD parameters to the collateralized exposure (collateral value after application of haircuts). The LGD setting for defaulted homogeneous portfolios are partially dependent on time after default and are either calibrated based on the Group’s multi-decade loss and recovery experience using statistical methods or for less significant portfolios certain LGD model input parameters (e.g. cure rates) are determined by expert judgement.

The third key input is the exposure at default over the lifetime of a financial asset which is modelled taking into account expected repayment profiles (e.g. linear amortization, annuities, bullet loan structures). Prepayment options are not modelled for all portfolios as they are not deemed material. The bank applies specific credit conversion factors (CCFs) in order to calculate an EAD value. Conceptually, the EAD is defined as the expected amount of the credit exposure to a borrower at the time of its default. In instances where a transaction involves an unused limit, a percentage share of this unused limit is added to the outstanding amount in order to appropriately reflect the expected outstanding amount in case of a borrower’s default. This reflects the assumption that for commitments, the utilization at the time of default might be higher than the current outstanding balance. In case a transaction involves an additional contingent component (i.e., guarantees) a further percentage share is applied as part of the CCF model in order to estimate the amount of guarantees drawn in case of default. The calibrations of such parameters are based on internal historical data and are either based on empirical analysis or supported by expert judgement and consider borrower and product type specifics. Where supervisory CCF values need to be applied for regulatory purposes, internal estimates are used for IFRS 9.

Expected Lifetime

IFRS 9 requires the determination of lifetime ECL for which the expected lifetime of a financial asset is a key input factor. Lifetime ECL represent default events over the expected life of a financial asset. The Group measures ECL considering the risk of default over the maximum contractual period (including any borrower’s extension options) over which the Group is exposed to credit risk.

Retail overdrafts, credit card facilities and certain corporate revolving facilities typically include both a loan and an undrawn commitment component. The expected lifetime of such on-demand facilities exceeds their contractual life as they are typically cancelled only when the Group becomes aware of an increase in credit risk. The expected lifetime is estimated by taking into consideration historical information and the Group’s credit risk management actions such as credit limit reductions and facility cancellation. Where such facilities are subject to an individual review by credit risk management, the lifetime for calculating ECL is 12 months. For facilities not subject to individual review by credit risk management, the bank applies a lifetime for calculating ECL of 24 months.

Interest Rate used in the IFRS 9 model

In the context of the ECL calculation, the Group applies in line with IFRS 9 an approximation of the effective interest rate (EIR), which is usually the contractual interest rate. The contractual interest rate is deemed to be an appropriate approximation, as the interest rate is consistently used in the ECL model, interest recognition and for discounting of the ECL and does not materially differ from the EIR.

Consideration of Collateralization in IFRS 9 Expected Credit Loss Calculation

The ECL model projects the level of collateralization for each point in time in the life of a financial asset. At the reporting date, the model uses the existing collateral distribution process applied in Deutsche Bank’s economic capital model. In this model, the liquidation value of each eligible collateral is allocated to relevant financial assets to distinguish between collateralized and uncollateralized parts of each financial asset. In the ECL calculation, the Group subsequently applies the aforementioned LGDs for secured and unsecured exposures to derive the ECL for the secured and unsecured part of the exposure separately.

For personal collateral (e.g. guarantees), the ECL model assumes that the relative level of collateralization remains stable over time. In the case of an amortizing loan, the outstanding exposure and collateral values decrease together over time. For physical collateral (e.g. real estate property), the ECL shall assume that the absolute collateral value remains constant. In case of an amortizing loan, the collateralized part of the exposure increases over time and the loan-to-value decreases accordingly.

Certain financial guarantee contracts are integral to the financial assets guaranteed. In such cases, the financial guarantee is considered as collateral for the financial asset and the benefit of the guarantee is used to mitigate the ECL of the guaranteed financial asset.

Forward Looking Information

Under IFRS 9, the allowance for credit losses is based on reasonable and supportable forward-looking information available without undue cost or effort, which takes into consideration past events, current conditions and forecasts of future economic conditions.

To incorporate forward looking information into the Group’s allowance for credit losses, the bank uses two key elements:

• – As its base scenario, the Group uses external survey-based macroeconomic forecasts (e.g. consensus views on GDP and unemployment rates). In addition, the scenario expansion model, which has been initially developed for stress testing, is used for forecasting macroeconomic variables that are not covered by external consensus data. All forecasts are assumed to reflect the most likely development of the respective variables. The Group regularly updates its forecasts for macro-economic factors during the quarter and reviews aspects of potential model imprecision (e.g. MEV parameters outside the historic range used for model calibration, if not already included in the model) as part of an MEV monitoring framework to assess if an overlay is required.
• – Statistical techniques are then applied to transform the base scenario projections into a probability distribution of the macroeconomic variables. These scenarios specify deviations from the baseline forecasts. The scenario distribution is then used for deriving multi-year PD curves for different rating and counterparty classes, which are applied in the ECL calculation and in the identification of significant deterioration in credit quality of financial assets as described above in the rating-related Stage 2 indicators

The Group's Risk and Finance Credit Loss Provision Forum monitors the impact of forward-looking information, including the latest macroeconomic variables, on a monthly basis and determines if any additional overlays are required. Although interest rates and inflation are not separately included in the MEVs, the economic impact of these risks is reflected in GDP growth rates, unemployment, equities and credit spreads as higher rates and inflation filter through these forecasts. As of December 31, 2022, the consensus data applied in the ECL model was deemed to have reflected the latest macroeconomic developments and uncertainties in the MEVs and no additional overlays were required.

As described earlier, the Group’s approach to reflect macroeconomic variables in the calculation of ECLs is to incorporate forecasts for the next two years, using eight discrete quarterly observations. After the period of eight quarters, the Group constructs forecasts based on macro-economic variables and its historic trends.

The tables below contain the macroeconomic variables included in the application of forward-looking information in the IFRS 9 model as of December 31, 2022 and as of December 31, 2021.

Forward-looking information applied

	December 31, 2022¹ ²
	Year 1 (4 quarter avg)	Year 2 (4 quarter avg)
Commodity - Gold	1,745.84	1,797.74
Commodity - WTI	90.19	88.79
Credit - CDX Emerging Markets	260.99	239.03
Credit - CDX High Yield	489.77	476.53
Credit - CDX IG	85.33	84.94
Credit - High Yield Index	4.46	4.31
Credit - ITX Europe 125	101.26	96.50
Equity - MSCI Asia	1,178	1,176
Equity - Nikkei	28,427	29,287
Equity - S&P500	3,933	4,011
GDP - Developing Asia	3.95 %	4.60 %
GDP - Emerging Markets	3.31 %	3.94 %
GDP - Eurozone	0.87 %	0.53 %
GDP - Germany	(0.26) %	1.00 %
GDP - Italy	0.32 %	0.68 %
GDP - USA	0.62 %	0.61 %
Real Estate Prices - US CRE Index	352.41	343.97
Unemployment - Eurozone	7.03 %	7.15 %
Unemployment - Germany	3.22 %	3.33 %
Unemployment - Italy	8.24 %	8.53 %
Unemployment - Japan	2.56 %	2.42 %
Unemployment - Spain	13.06 %	12.98 %
Unemployment - USA	4.05 %	4.75 %

¹ MEV as of December 12, 2022 which barely changed until December 30, 2022

² Year 1 equals fourth quarter of 2022 to third quarter of 2023, Year 2 equals fourth quarter of 2023 to third quarter of 2024.

	December 31. 2021¹ ²
	Year 1 (4 quarter avg)	Year 2 (4 quarter avg)
Commodity - Gold	1,764.58	1,696.51
Commodity - WTI	73.19	68.21
Credit - CDX Emerging Markets	231.80	268.64
Credit - CDX High Yield	353.42	399.62
Credit - CDX IG	59.53	63.98
Credit - High Yield Index	3.95	4.46
Credit - ITX Europe 125	61.37	69.93
Equity - MSCI Asia	1,543	1,514
Equity - Nikkei	29,673	30,764
Equity - S&P500	4,777	5,033
GDP - Developing Asia	3.78 %	6.26 %
GDP - Emerging Markets	3.72 %	5.38 %
GDP - Eurozone	4.67 %	2.91 %
GDP - Germany	3.35 %	2.86 %
GDP - Italy	5.17 %	2.33 %
GDP - USA	4.46 %	2.79 %
Real Estate Prices - US CRE Index	348.86	377.26
Unemployment - Eurozone	7.41 %	7.07 %
Unemployment - Germany	3.13 %	2.83 %
Unemployment - Italy	9.18 %	8.92 %
Unemployment - Japan	2.73 %	2.53 %
Unemployment - Spain	14.26 %	13.66 %
Unemployment - USA	4.05 %	3.68 %

¹ MEV as of 31 December 2021; MEV outside the calibrated range were adjusted either in the model or via a management overlay as discussed further below.

² Year 1 equals fourth quarter of 2021 to third quarter of 2022, Year 2 equals fourth quarter of 2022 to third quarter of 2023.

Focus areas in 2022

Deutsche Bank’s macroeconomic environment in 2022 was influenced by geopolitical uncertainty and a substantial increase in energy prices, supply chain disruptions, rising interest rates, inflation, and a deteriorating economic outlook for major economies. However, by the end of fourth quarter 2022, the economic outlook had stabilized as a mild winter reduced energy concerns in Europe, energy prices receded, and expectations that inflation may have peaked in Europe and the U.S. were observed. While the outlook remains challenging, risk of a severe further deterioration has eased significantly.

To ensure that Deutsche Bank’s ECL model was taking into account all the uncertainties in the macroeconomic environment throughout 2022, the Group reviewed emerging risks to assess its potential downside and to manage the bank’s credit strategy and risk appetite. Overall, the actions taken as a result of these reviews ensured the bank was adequately provisioned for its expected credit losses as of December 31, 2022.

Areas of focus in 2022 included several deep dives into first and second order risks resulting from the war in Ukraine. This included comprehensive analysis of the bank’s exposure in Russia and Ukraine, along with the impact of increasing energy prices and inflation on wider portfolios. In the first and second quarter of 2022, most exposures to Russian clients were moved to the watchlist and transferred to Stage 2. The Group’s net loan exposure to Ukraine is immaterial (€ 64 million as of December 31, 2022 and € 42 million as of December 31, 2021).

Risks related to the increase in energy prices were managed by closely monitoring industries and geographies sensitive to energy prices (e.g. utilities), along with enhanced engagement with clients in the most vulnerable sectors via surveys and detailed discussions to assess their respective risk profile and the overall impact in such sectors. As a result of these actions, the Group was able to identify if any of these vulnerable exposures experienced a significant increase in credit risk in a timely and pro-active manner. As a result, some client relationships were moved onto the watchlist and into Stage 2.

Overall, Deutsche Bank has a conservative risk profile based on a diversified loan book across geographic regions, businesses and sectors. Deutsche Bank’s loan book in Germany amounts to € 235 billion as of December 31, 2022 and the bank’s confidence in the portfolio’s quality is driven by conservative lending standards and well-diversified exposures. Around 75 % of the German loan book is within the Private Bank and nearly 90 % thereof relates to low-risk German retail mortgages. In the German mortgage market, clients typically lock in fixed rates for 10 or more years, which is reflected in the bank’s long-term, fixed rate mortgage portfolio with a loan-to-value of 66 % based on current market values. The bank also benefits from structural elements as e.g. a low maturity profile in 2023 reduces refinancing pressure in Leveraged Lending.

The Group’s German corporate loan book is € 63 billion as of December 31, 2022, consisting mainly of trade finance and commercial lending and is diversified across a large number of clients (i.e. the top 15 names account for only 6 % of the portfolio). Credit quality in the German corporate portfolio is high with 71 % of loans rated investment grade and with only 2 % of loan exposures in Stage 3 as of December 31, 2022. The Group believes the ECL model has adequately provisioned for the German retail and corporate loan portfolios as the IFRS 9 model has captured rating downgrades, significant increases in credit risk, e.g. by moving borrowers to the watchlist (Stage 2) when the criteria was met or identified those counterparties meeting the definition of default.

The Group also performed reviews in the Corporate Bank and Investment Bank focusing on vulnerable portfolios and/or counterparties determined as potentially vulnerable to rising interest rates due to higher debt levels and/or low interest coverage ratios. The reviews have been designed to build-in an additional layer of portfolio/counterparty reviews supporting the strong credit governance and processes. In particular, credit officers assessed the potential need for additional structural enhancements or collateralization and whether additional counterparties needed to be added to the credit watchlist based on the above mentioned concerns. These exercises included among others, but not exclusively, Commercial Real Estate (CRE) and the Leveraged Lending portfolios. Deutsche Bank’s in focus CRE portfolio amounts to € 33 billion or 7 % of the bank’s total loan book and comprises non-recourse lending within the core CRE business units in the Investment Bank and Corporate Bank. The bank’s CRE lending activities are mainly first lien mortgage-secured and structured with moderate loan-to-values. 51 % of CRE exposures are in the US, 36 % in Europe and 13 % in Asia and loan originations are primarily focused on assets in liquid regional locations such as top-tier gateway cities. The portfolio is diversified by property type, with the largest concentration of 34 % in office space, while hospitality and retail account for only 12 % and 11 %, respectively. Weighted average LTV is around 61 % in the Investment Bank and 53 % in the Corporate Bank.

The Group’s Leveraged Lending Leveraged Debt Capital Market exposure in the Investment Bank’s Origination & Advisory portfolio is € 4 billion, representing just 1 % of the bank’s total loan book, is well diversified across industry sectors without any undue concentration risks with the top 10 names accounting for 11 % of the portfolio on a gross notional basis. Around 79 %of the exposure is in the form of first line secured credit facilities, mostly of revolving nature and the remaining 21 % is asset based lending, which is almost entirely U.S. based and has a negligible loss history. Where relevant, CRE and Leverage Lending clients were transferred to Stage 2 (e.g. watchlist criteria met) or Stage 3 if in default situation, in line with our well established credit processes.

The Group’s loan book exposure to China as of December 31, 2022 was € 6.1 billion or 1 % of the total loan book of which only 7 % and 3 % are in Stage 2 and Stage 3, respectively. The Group reviewed the exposures in China on a more regular basis due to the elevated geopolitical risks and resulted in a further tightening of the Bank’s risk appetite to Chinese clients in the most vulnerable sectors and enhanced monitoring where deemed necessary.

Results from the above reviews have been discussed in relevant governance forums such as the Enterprise Risk Committee and the Credit Risk Appetite and Management Forum and relevant actions and measures were taken to mitigate the risks and ensure appropriate ECLs were provisioned. For example, where necessary clients‘ ratings were updated to reflect the latest macroeconomic developments, clients were moved to the watchlist (Stage 2), forbearance measures have been negotiated, credit limits were reduced and where possible collateralization was increased. Overall, the Group believes based on its day-to-day risk management activities and the deep dives described above it has adequately provided for its ECL provision as of December 31, 2022. However, the section below further considers whether any additional overlays were required as of year end 2022.

Management overlays applied to the IFRS 9 model output

The Group regularly reviews key inputs into the ECL calculation and discusses potential model imprecision to assess the need for corrective measures in the form of overlays. Due to the challenging geopolitical environment in 2022, the Group also considered if there were any uncertainties in the macroeconomic environment not included in the model. In the following section, the Group provides details on its management overlays recorded as of December 31, 2021 and its developments to December 31, 2022.

Development of overlays from December 31, 2021 to December 31, 2022

in € m. (unless stated otherwise)		Overlays as of December 31, 2021	New Overlays	Discontinued overlays	Overlays as of December 31, 2022
Overlay description	Impact on
Construction Risk following increased prices for building materials	Mortgage portfolios in the Private Bank in Stage 1 and 2	15	0	(15)	0
Model calibration (MEV outside calibrated range of the FLI model)	Financial assets in Stage 1 and 2	56	0	(56)	0
Recalibrations required due to the new Definition of Default	Financial assets primarily in the Private Bank in Stage 3	(57)	(35)	0	(92)
Uncertainty related to Russia/Ukraine¹	All financial assets in Stage 1 and 2	0	127	(127)	0
Model calibration (WTI oil price Index disabled for one portfolio)	Financial assets in Stage 1 and 2 in the Investment Bank	0	39	(39)	0
Total		14	131	(237)	(92)

¹ The overlay recorded with regards to the uncertainty related to Russia/Ukraine in the first quarter of 2022 in the amount of € 44 million was released in the second quarter 2022; the overlay recorded with regards to the uncertainty related to Russia/Ukraine in the second quarter of 2022 in the amount of € 83 million was released in the third quarter 2022

The Group applied the following overlays to the IFRS 9 model output as of December 31, 2021 until the end of December 31, 2022.

Construction Risk following increased prices for building materials

In 2021, the Group record a € 15 million overlay to address the risk of budget overruns due to unavailable or significantly more expensive building materials. The overlay was released in first quarter 2022 as the risk no longer existed and construction risk was factored into the lending criteria.

Model calibration (MEV outside the calibrated range)

The Group applied a management overlay to address the model uncertainty associated with extreme year on year MEV projections throughout the COVID-19 pandemic, in particular GDPs, which were identified as being outside the calibrated range of the FLI model. Since the model was not calibrated based on such extreme MEV movements, the Group was concerned that the model underestimated expected credit losses in such situations. As of December 31, 2021, the overlay was € 56 million and as the MEVs moved into the calibrated range in the first half of 2022, the overlay was fully released. The releases resulted in a decrease of the Group’s allowance for credit losses.

Recalibrations required due to the new definition of default

In 2021, the Group implemented the new definition of default which is the trigger for Stage 3. The implementation of the new definition of default mainly affected the Private Bank, where the Stage 3 population in homogeneous portfolios increased. As the change in definition does not materially impact the total loss expectation of these portfolios, this change resulted in an overstatement of Stage 3 provisions as the related LGD parameters were not updated in the model. The next LGD recalibration has been rescheduled to the second half of 2023 as additional empirical data is needed for the statistical recalibration. The overlay will remain until the recalibration is completed. The estimate of the recalibration effect has been refined in the first quarter 2022 and was increased to € 92 million and remains the same as of December 31, 2022. The € 92 million overlay results in a decrease in the Group’s allowance for credit losses but is offset by the overstatement of Stage 3 provisions calculated in the ECL model.

Uncertainty related to Russia/Ukraine

In the first quarter 2022, the Group introduced a management overlay amounting to € 44 million which was specific to the overall uncertainty associated with the economic outlook from the war in Ukraine and was released in second quarter 2022 once the uncertainty was included in the MEVs. However, towards the end of June 2022 the macro-economic outlook weakened further. The main reason was the growing concerns over the gas supply from Russia to Europe (Germany in particular) and market expectation of significantly more aggressive monetary tightening in the U.S. and other markets to combat persistent inflation. Both developments accelerated in late June and due to timing was not reflected in the consensus forecast. As a result, the bank recorded an overlay to increase the allowance for credit losses by € 83 million and was released in third quarter 2022 once reflected in the MEV forecasts.

During the third quarter of 2022, the Group carefully monitored the suspension of Russian gas to Germany via the Nord Stream 1 pipeline in early September and the attacks on Nord Stream 1 and Nord Stream 2 pipelines in late September. As uncertainty related to gas supplies was included in the consensus data as of September 30, 2022, no overlay was recorded in the third quarter.

As mentioned in the Forward-Looking Information section above, no overlays were deemed necessary as of December 31, 2022 for uncertainties related to the war in Ukraine, geopolitical events, rising interest rates or inflation as these uncertainties were reflected in the MEVs.

Model calibration (WTI oil price Index)

The Group introduced a management overlay to address model implications related to the incorporation of forward-looking information for oil prices in the ECL calculation. In the past, increases in oil prices were typically demand driven and reflective of a positive economic environment, which is why the IFRS 9 model was designed to release ECL provisions in this scenario. The increase in oil prices in first quarter 2022 was driven by supply risks and impacts from the war in Ukraine. Although higher oil prices have a positive effect on industries such as oil and gas producers, higher oil prices driven by supply issues have a negative impact on other industries and portfolios. Therefore a € 42 million overlay was recorded as of March 31, 2022 to reverse the release of provisions certain industries based on the increase in the WTI Oil price Index. The overlay was reduced to € 39 million for the period ended June 30, 2022, to € 27 million for the period ended September 30, 2022.and was fully released at the end of 2022. The overlay increased the Group’s allowance for credit losses.

Overall assessment

In assessing whether the Group requires any additional overlays, it regularly reviews for evolving or emerging risks, especially in the current geopolitical environment. Similar to the measures included above in the Focus areas in 2022, these measures include client surveys and interviews, along with analysis of portfolios across businesses, regions and sectors. In addition, the Group regularly reviews and validates key model inputs and assumptions (including those in feeder models) and ensures where expert judgement is applied, it is in line with the Group’s risk management framework. As of December 31, 2022, the Group did not identify any additional downside risks not reflected in the IFRS 9 ECL model and did not identify any model weaknesses that would require an additional overlay other than the existing overlay related to the new definition of default.

Model Sensitivity

The Group has identified three key model assumptions included in the IFRS 9 model. These include forward looking macroeconomic variables, the quantitative criteria for determining if a borrower has incurred a significant increase in credit risk and transferred to Stage 2, and the LGD setting on homogenous portfolios in Stage 3. Below the bank provides sensitivity analysis on the potential impact if these key assumptions applied in the ECL model were to deviate from the bank’s base case expectations.

Macroeconomic Variables

The sensitivity of the ECL model with respect to potential changes in projections for key MEVs is shown in the tables below, which provides ECL impacts for Stages 1 and 2 from one sigma downward and upward shifts applied separately to each group of MEV as of December 31, 2022 and December 31, 2021. A sigma shift is a standard deviation used in statistics and probability calculations and is a measure of the dispersion of the values of a random variable. Each of these groups consists of MEVs from the same category:

• – GDP growth rates: includes USA, Eurozone, Germany, Italy, Developing Asia, Emerging Markets
• – Unemployment rates: includes USA, Eurozone, Germany, Italy, Japan, Spain
• – Equities: S&P500, Nikkei, MSCI Asia
• – Credit spreads: ITX Europe 125, High Yield Index, CDX IG, CDX High Yield, CDX Emerging Markets
• – Real Estate: Commercial Real Estate Price Index
• – Commodities: WTI oil price, Gold price

Although interest rates and inflation are not separately included in the MEVs above, the economic impact of these risks is adequately reflected in other macroeconomic variables, such as GDP growth rates, unemployment, equities and credit spreads as higher rates and inflation would filter through these forecasts and be included in the ECL model and sensitivity analysis below.

In addition, the sensitivity analysis only includes the impact of the aggregated MEV group (i.e. potential correlation between different MEV groups or the impact of management overlays is not taken into consideration). ECLs for Stage 3 are not affected and not reflected in the following tables as its calculation is independent of the macroeconomic scenarios.

Sensitivity impact is significantly higher as of December 31, 2022 compared to December 31, 2021, due to the overall higher level of ECL on which basis the sensitivity analysis was performed, taking into account the continued economic uncertainty from the effects of the war in Ukraine, geopolitical environment, rising interest rates and inflation as of December 31, 2022.

IFRS 9 – Sensitivities of Forward-Looking Information applied on Stage 1 and Stage 2 – Group Level

	December 31, 2022
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(83.3)	(1)pp	101.4
Unemployment rates	(0.5)pp	(40.8)	0.5pp	58.0
Real estate prices	5%	(5.6)	(5)%	6.0
Equities	10%	(15.8)	(10)%	19.6
Credit spreads	(40)%	(37.9)	40%	42.6
Commodities¹	10%	(14.8)	(10)%	15.6

¹ Here the sign of the shift applies to oil prices changes. Gold price changes have the opposite sign. 1pp (percentage point), e.g. GDP shifts from 3% to 4% // 1% (percentage change), e.g. Real estate price shifts from 100 to 101.

	December 31, 2021
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(49.4)	(1)pp	55.5
Unemployment rates	(0.5)pp	(23.8)	0.5pp	25.4
Real estate prices	5%	(3.9)	(5)%	4.2
Equities	10%	(7.2)	(10)%	9.4
Credit spreads	(40)%	(20.9)	40%	23.5
Commodities	10%	(15.0)	(10)%	16.2

In the second and third quarter of 2022, the Group conducted a variety of scenarios to assess the downside impact should the cessation of Russia gas supplies to Europe lead to a sharper than expected economic slowdown and the emergence of more widespread defaults across European corporate and household exposures. Based on such factors, the Group estimated that such an event would potentially result in an additional allowance for credit losses of up to approximately bps  20  basis points over an 18-month period. As of yearend 2022, the Group acknowledged that the aforementioned scenario did not materialize and is no longer deemed plausible. Germany’s gas storage was sufficiently filled to supply businesses and households over the winter, energy prices significantly declined, and alternate energy sources were identified.

The Group considered whether there were any other specific downside scenarios it should consider in its sensitivity analysis, but as the uncertainty related to interest rates and inflation is already included in the MEVs and the bank did not observe any specific vulnerable credit risk concentrations in its portfolios, the Group believes the one standard sigma shift provides the best information on the model’s ECL sensitivity.

At the divisional level, the sensitivity analysis below was performed for the year ended December 31, 2022 and 2021, respectively, and revealed GDP growth rates, credit spreads and commodities prices to be the dominant factors for the Investment Bank, whereas the model sensitivity for the Corporate Bank and Private Bank is mainly associated with changes in GDP growth rates and unemployment rates. The model sensitivity table for the Private Bank shows GDP growth rates and unemployment rates only, as the key MEVs relevant to the underlying portfolios.

IFRS 9 – Sensitivities of Forward-Looking Information applied on Stage 1 and Stage 2 - Corporate Bank

	December 31, 2022
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(21.7)	(1)pp	24.6
Unemployment rates	(0.5)pp	(12.2)	0.5pp	14.0
Real estate prices	5%	(1.1)	(5)%	1.1
Credit spreads	(40)%	(7.5)	40%	9.1
Commodities¹	10%	(4.3)	(10)%	4.6

¹Here the sign of the shift applies to oil prices changes. Gold price changes have the opposite sign.

	December 31, 2021
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(12.5)	(1)pp	13.7
Unemployment rates	(0.5)pp	(8.9)	0.5pp	9.6
Real estate prices	5%	(0.5)	(5)%	0.5
Credit spreads	(40)%	(4.3)	40%	4.9
Commodities	10%	(4.5)	(10)%	5.0

IFRS 9 – Sensitivities of Forward-Looking Information applied on Stage 1 and Stage 2 - Investment Bank

	December 31, 2022
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(35.3)	(1)pp	36.9
Unemployment rates	(0.5)pp	(5.3)	0.5pp	6.1
Real estate prices	5%	(4.5)	(5)%	4.8
Equities	10%	(5.8)	(10)%	7.3
Credit spreads	(40)%	(26.3)	40%	28.5
Commodities¹	10%	(9.8)	(10)%	10.3

¹Here the sign of the shift applies to oil prices changes. Gold price changes have the opposite sign.

	December 31, 2021
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(24.5)	(1)pp	27.7
Unemployment rates	(0.5)pp	(3.7)	0.5pp	4.2
Real estate prices	5%	(3.4)	(5)%	3.6
Equities	10%	(2.4)	(10)%	3.1
Credit spreads	(40)%	(14.4)	40%	15.8
Commodities	10%	(10.1)	(10)%	10.8

IFRS 9 – Sensitivities of Forward-Looking Information applied on Stage 1 and Stage 2 - Private Bank

	December 31, 2022
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(21.8)	(1)pp	34.5
Unemployment rates	(0.5)pp	(20.7)	0.5pp	34.9

	December 31, 2021
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impact in € m.	Downward shift	ECL impact in € m.
GDP growth rates	1pp	(10.0)	(1)pp	10.7
Unemployment rates	(0.5)pp	(9.7)	0.5pp	9.8

Impact of Lifetime Expected Credit Losses for Stage 1 borrowers

As described earlier, the Group uses a mixture of quantitative and qualitative criteria to determine significant increase in credit risk which require, for affected borrowers, a move to lifetime ECL (Stage 2). If for all Stage 1 borrowers Deutsche Bank were to record lifetime expected credit losses, the Group’s allowance for credit losses amounting to € 5.6 billion as of December 31, 2022 and € 5.4 billion as of December 31, 2021 would increase by approximately 44 % as of yearend 2022 and as of yearend 2021.

Stage 3 LGD setting

The Group’s allowance for credit losses in Stage 3 for the homogeneous portfolios amounts to € 1.9 billion as of December 31, 2022 and € 2.2 billion as of December 31, 2021. The key driver in determining the ECL provision is the loss given default estimate, which differs by individual portfolios. Loss given default is influenced by recovery rates, proceeds from the sale of collateral, and cure rates. Some of the drivers for different portfolios include elements of expert judgment and in particular on expected cure rates. If the LGD for all homogeneous portfolios were to increase by 1%, then Stage 3 ECL would increase as of December 31, 2022 by approximately € 19 million (thereof € 11 million in Germany, € 5 million in Italy and € 2 million in Spain), and by approximately € 22 million as of December 31, 2021 (thereof € 11 million in Germany, € 7 million in Italy and € 2 million in Spain).

IFRS 9 Model results

In 2022, provision for credit losses was € 1.2 billion which is significantly higher than the € 515 million recorded for the year ended 2021. The increase is reflecting a deterioration of the macroeconomic environment following the war in Ukraine and increased number of impairment events compared to an overall more benign environment in 2021. The total provisions in 2022 includes € 114 million related to clients in Russia and Ukraine compared to € 10 million in 2021.

In 2022, € 204 million provision for credit losses were related to Stage 1 and 2 and € 1.0 billion to Stage 3, this compares to € 218 million release of Stage 1 and 2 provisions and an € 734 million in stage 3 in 2021. The increase of Stage 1 and 2 provisions was primarily driven by the deterioration of macroeconomic parameters, the increase of Stage 3 provisions was affecting all regions and sectors.

In regards to the Business Divisions, the Corporate Bank recorded an increase of provisions for credit losses of € 335 million in 2022 versus a € 3 million release in 2021. The year-over-year increase was primarily driven by Stage 1 and 2 provisions following a deteriorated macro-economic outlook, an increased number of impairments compared to a very benign development of provisions in the prior year. The Investment Bank recorded an increase of provisions for credit losses of € 319 million in 2022 versus € 104 million in 2021. The increase was mainly driven by an increased number of new impairments, whilst the prior year benefitted from a low number of impairment events which were further mitigated by larger Stage 3 releases. The Private Bank recorded an increase of provisions for credit losses of € 583 million in 2022 versus € 446 million reported in 2021. The increase was mainly driven by Stage 1 and 2 provisions following a deteriorated macro-economic outlook whilst previous year’s period was benefitting from an overall benign macroeconomic environment.

The amounts recognized in the allowance for credit losses in relation to climate-related risks are deemed to be immaterial at the end of December 31, 2021 and as of December 31, 2022.

For details on the Group’s accounting policy related to IFRS 9 Impairment, please refer to Note 1 - Significant Accounting Policies and Critical Accounting Estimates of the Consolidated Financial Statements.

Exposure to Russia

One of management’s key focus areas in 2022 was the identification, assessment and management of direct and indirect impacts of the war in Ukraine. Since 2014, the Group has significantly reduced its footprint in Russia due to the heightened risk of sanctions and potential countermeasures. The bank has continued to reduce its risk positions through 2022 as a result of active exposure management, client repayments and roll-offs from guarantees.

As of December 31, 2022, the Group’s loan exposure to Russia amounted to € 806 million on a gross basis (€ 1,397 million as of December 31, 2021), which represents approximately 0.2 % of the total loan book (0.3 % as of December 31, 2021). On a net basis, after risk mitigants such as Export Credit Agency insurance and Private Risk Insurance, the loan exposure amounted to € 379 million (€ 594 million as of December 31, 2021). Additional undrawn commitments amounted to € 78 million (€ 961 million as of December 31, 2021) and are subject to contractual drawdown protection and parental guarantees for multinational corporates (“MNCs”). The majority of loan exposure relates to large Russian companies with material operations and cash-flow outside of Russia. Such existing loans may be provided onshore by DB Moscow, or offshore by other Group entities outside of Russia. Wealth Management has granted offshore loans to counterparties with a Russian nexus, collateralized in line with the Group’s policies. In line with the overall group strategy, exposures to Russian nexus clients have been reduced significantly during the course of 2022.

As of December 31, 2022, the Group had € 76 million contingent exposure to Russia via written financial and trade guarantees (€ 541 million as of December 31, 2021). Residual derivative exposures to Russia are small as all major positions have been unwound with the Group being a net payer on a mark-to-market basis.

The bank’s overall net loan exposure to Ukraine is € 64 million as of December 31, 2022 (€ 42 million as of December 31, 2021.

Some of the aforementioned factors have resulted in immediate portfolio impacts in 2022, including negative rating migration on Russian names, resulting in higher credit risk weighted assets, as well as moderate increases in provisions for credit losses and higher impacts from prudential valuation. More broadly, in instances where there is a concern that counterparty credit quality has deteriorated or appears likely to deteriorate, the respective exposure has been placed on the “watchlist” and included in Stage 2. The objective of this early warning system is to address potential problems while adequate options for action are still available. As of December 31, 2022, beyond Russian names which have been added to the watchlist, the Group has not observed any material structural credit deterioration across other portfolios or industries related to Russia.

The following table provides an overview of total Russian exposures, including overnight deposits with the Central Bank of Russia in the amount of € 0.8 billion as of December 31, 2022 (€ 0.5 billion as of December 31, 2021) and other receivables, which are subject to IFRS 9 impairment, and correspondent allowance for credit losses by stages as of December 31, 2022 and December 31, 2021.

Breakdown of total exposure and allowance for credit losses by stages

	Dec 31, 2022			Dec 31, 2021
in € m.	Total Exposure	Allowance for Credit Losses1	Total collateral and guarantees	Total Exposure	Allowance for Credit Losses1	Total collateral and guarantees
Stage 1	209	0	59	3,198	1	648
Stage 2	1,182	10	375	332	2	263
Stage 3	336	68	152	3	0	2
Total	1,726	79	586	3,534	3	913

¹ Allowance for credit losses do not include allowance for country risk amounting to € 11 million as of December 31, 2022 and € 0 million as of December 31, 2021

Total exposure of € 1.7 billion consists of above mentioned € 0.8 billion loan exposure to Russia, € 78 million of undrawn commitments and € 0.8 billion of unsecured overnight deposits in Rubles with the Central Bank of Russia (which continues to be reflected in Stage 2 as of December 31, 2022); the residual unsecured exposure, excluding the unsecured overnight deposits in Rubles with the Central Bank of Russia, is mainly driven by undrawn commitments which are subject to ECA coverage and contractual drawdown protection.

Market Risk

The Group has managed its market risk to Russia by performing regular risk assessments of its risk profile. To mitigate a broader contagion risk, action was taken in second quarter of 2022 to reduce direct exposure prior to and immediately after events unfolded. This was achieved by entering into additional hedges and selective de-risking. The Group continues to closely monitor the situation by performing further contagion stress testing on different scenarios. On September 12, 2022 Russia CDS auction was completed and the recovery rate was set at 56 %with the settlement process completed on September 23, 2022. As of December 31, 2022, Deutsche Bank continues to maintain overall low levels of direct market risk exposure to Russia.

Russian operations

The Group has an operating subsidiary in Russia, OOO "Deutsche Bank" (DB Moscow), which provides corporate banking services to local subsidiaries of international companies. As of December 31, 2022, the Group’s total capital position in Russia was € 0.3 billion (€ 0.2 billion as of December 31, 2021) and ~40 %of this capital was hedged against FX risk (~ 80 % as of December 31, 2021). Total assets of DB Moscow amounted to € 1.1 billion (€ 1.5 billion as of December 31, 2021), of which approximately € 0.8 billion (Russian Ruble equivalent, € 0.5 billion as of December 31, 2021) was deposited with the Central Bank of Russia. Local operations are fully self-funded with no cross-border Group funding required.

The Group also operates a technology service centre in Russia, OOO Deutsche Bank TechCentre (DBTC), which is one of several technology centers around the world. DBTC is focused on delivering “change-the-bank” activities for the Investment Bank and the Corporate Bank. The Group continues to de-risk its operations in DBTC by reassigning tasks to other technology centers around the world. The Group have stress-tested the ability of the bank’s other technology centers around the world, including in Asia, to cover the Russian service center’s development capabilities. There is no data or code maintained in the Russian Tech-Centre.

In 2022, the Group established its next Technology Centre in Berlin, Germany. The center will primarily support the ambitions of the Investment Bank and the Corporate Bank through application development and the integration of new technologies. While the Group will continue to grow the Berlin Technology Centre with local hires, there have also been transfers of resources from its Tech Centre in Russia to Berlin as it consolidates the hub for Artificial Intelligence and Machine Learning expertise, which represents a key opportunity for the Group to create significant value for its clients and further enhance the efficient running of its operations.

The Group has accrued for the committed relocation costs of certain resources from DBTC.

Compliance and Anti Financial Crime risks

The Group continues to rapidly adapt to the sanctions landscape that has and continues to evolve after the invasion of Ukraine. After the implementation of sanctions, the Bank has reacted with several actions including but not limited to prompt updates of the Bank’s relevant lists for Name List Screening and Transaction Filtering, the formulation and dissemination of guidance to the businesses, engagement with governmental bodies on interpretive issues and the seeking of special licenses to allow for orderly wind-downs of open positions. The AFC function, and specifically its Sanctions & Embargoes department, plays a crucial role in the current situation given the rapidly changing regulatory environment and provides, where needed, up-to-date guidance to the businesses.

IFRS 9 Application EBA Guidance Default Forbearance IFRS 9 COVID-19 [text block]

IFRS 9 - Application of EBA guidance regarding Default, Forbearance and IFRS 9 in light of COVID-19 measures EBA’s “Statement on the application of the prudential framework regarding Default, Forbearance and IFRS 9 in light of COVID‑19 measures” published on March 25, 2020 states that institutions are expected to use a degree of judgement and distinguish between borrowers whose credit standing would not be significantly affected by the current situation in the long term, and those who would be unlikely to restore their creditworthiness. The Bank performed portfolio reviews and applied this regulatory guidance to a number of clients mainly in the Investment Bank and Corporate Bank. EBA is further of the view that the public and private moratoria, as a response to COVID-19 pandemic, do not have to be automatically classified as forbearance if the moratoria are not borrower specific, based on the applicable national law or on an industry or sector-wide private initiative agreed and applied broadly by relevant credit institutions. Deutsche Bank has introduced this guidance into its internal risk management processes.

Legislative and non-legislative moratoria and public guarantee schemes in light of COVID-19 pandemic [text block]

Legislative and non-legislative moratoria and public guarantee schemes in light of COVID-19 pandemic In 2020, the European Banking Association (EBA) issued a “Statement on the application of the prudential framework regarding Default, Forbearance and IFRS 9 in light of COVID-19 measures”, along with guidance on legislative and non-legislative moratoria. The following table provides an overview of expired loans and advances subject to EBA-compliant moratoria, loans and advances subject to COVID-19 related forbearance measures and newly originated loans and advances subject to a public guarantee scheme as of December 31, 2022 and December 31, 2021. There have been no newly originated loans and advances subject COVID-19 related forbearance measures and public guarantee scheme since December 31, 2021. Breakdown of COVID-19 related measures by stages Dec 31, 2022 Legislative and non-legislative Moratoria COVID-19 related forbearance measures Public guarantee schemes in € m. Gross Carrying Amount Expected Credit Losses Gross Carrying Amount Expected Credit Losses Gross Carrying Amount Expected Credit Losses Stage 1 4,377 (6) 1,835 (2) 2,159 (3) Stage 2 1,060 (22) 1,027 (16) 816 (9) Stage 3 541 (152) 337 (77) 190 (37) Total 5,978 (180) 3,199 (95) 3,165 (49) Dec 31, 2021 Legislative and non-legislative Moratoria COVID-19 related forbearance measures Public guarantee schemes in € m. Gross Carrying Amount Expected Credit Losses Gross Carrying Amount Expected Credit Losses Gross Carrying Amount Expected Credit Losses Stage 1 5,381 (10) 3,330 (6) 3,079 (2) Stage 2 1,288 (30) 2,602 (31) 770 (9) Stage 3 698 (162) 965 (122) 103 (14) Total 7,368 (202) 6,897 (158) 3,952 (25) COVID-19 related forbearance measures: As of December 31, 2022, COVID-19 forbearance measures have been granted to € 3.2 billion outstanding loans and advances. As of December 31, 2022, over 88 %of clients are still performing and the Bank continues to remain at a stable ECL level. All forborne loans and advances are required to be classified as forborne until a 24-months’ probation period has been reached. EBA-compliant moratoria can be divided into legislative moratoria, which are instituted by the Government and non-legislative moratoria granted by a group of financial institutions. Moratoria were mainly granted in Germany, Italy and Spain and expired by year end 2020 resp. 2021. More than 95 % of these clients who took advantage of moratoria have resumed their payments. As of December 31, 2021, less than € 30 million were still active. During 2022, the number of clients and volumes under moratoria have further significantly reduced due to repayments. As of December 31, 2022, nearly all moratoria have expired, those that are still active are € 4.5 million. Newly originated loans and advances subject to a public guarantee scheme: The Group has originated approximately € 3.4 billion of loans under the public guarantee scheme as of December 31, 2022. Approximately € 1.7 billion of loans were granted in Germany via programs sponsored by KfW, of which, € 0.2 billion were derecognized as the terms of the loan and guarantee met the criteria for derecognition under IFRS 9, and € 1.7 billion were originated in Spain. As of December 31, 2022, 94 %of the loans that were granted public guarantees continue to make regular repayments.

Measuring credit risk paragraph 1 [text block]

Measuring Credit Risk Credit risk is measured by credit rating, regulatory and internal capital demand and key components mentioned below. The credit rating is an essential part of the bank’s underwriting and credit process and provides – amongst others – a cornerstone for risk appetite determination on a counterparty and portfolio level, credit decision and transaction pricing as well the determination of regulatory capital demand for credit risk. Each counterparty must be rated and each rating has to be reviewed at least annually. Ongoing monitoring of counterparties helps to keep ratings up-to-date. A credit rating is a prerequisite for any credit limit established/ approved. For each credit rating the appropriate rating approach has to be applied and the derived credit rating has to be established in the relevant systems. Different rating approaches have been established to best reflect the specific characteristics of exposure classes, including specific product types, central governments and central banks, institutions, corporates and retail. Counterparties in the bank’s non-homogenous portfolios are rated by Deutsche Bank’s independent Credit Risk Management function. Country risk related ratings are provided by ERM Risk Research. Deutsche Bank’s rating analysis is based on a combination of qualitative and quantitative factors. When rating a counterparty Deutsche Bank applies in-house assessment methodologies, scorecards and the bank’s 21-grade rating scale for evaluating the creditworthiness of the bank’s counterparties.

Measuring credit risk paragraph 2 [text block]

Besides the credit rating, which is a key component Deutsche Bank applies for managing the bank’s credit portfolio, including transaction approval and the setting of risk appetite, Deutsche Bank establishes credit limits for all credit exposures. Credit limits set forth maximum credit exposures Deutsche Bank is willing to assume over specified periods. In determining the credit limit for a counterparty, Deutsche Bank considers the counterparty’s credit quality by reference to its internal credit rating. Credit limits and credit exposures are both measured on a gross and net basis where net is derived by deducting hedges and certain collateral from respective gross figures. For derivatives, Deutsche Bank looks at current market values and the potential future exposure over the relevant time horizon which is based upon the bank’s legal agreements with the counterparty. Deutsche Bank also takes into consideration the risk-return characteristics of individual transactions and portfolios. Risk-return metrics explain the development of client revenues as well as capital consumption.

Managing and Mitigation of credit risk paragraph 1 [text block]

Managing and mitigation of credit risk Managing credit risk on counterparty level Credit-related counterparties are principally allocated to credit officers within credit teams which are organized by type of counterparty (such as financial institutions, corporates or private individuals), economic area (e.g., Emerging Markets) or product (Structured Credit) and supported by dedicated rating analyst teams where deemed necessary. The individual credit officers have the relevant expertise and experience to manage the credit risks associated with these counterparties and their associated credit related transactions. For retail clients, credit decision making and credit monitoring is highly automated for efficiency reasons. Credit Risk Management has full oversight of the respective processes and tools used in these highly automated retail credit processes. It is the responsibility of each credit officer to undertake ongoing credit monitoring for their allocated portfolio of counterparties. Deutsche Bank also has procedures in place intended to identify at an early-stage credit exposures for which there may be an increased risk of increased risk/ loss. In instances where Deutsche Bank has identified counterparties where there is a concern that the credit quality has deteriorated or appears likely to deteriorate to the point where they present a heightened risk of default / loss, the respective counterparty is generally placed on the “Watchlist”. Deutsche Bank aims to identify those counterparties well in advance that, on the basis of the application of the bank’s risk management tools, demonstrate the likelihood of problems well in advance in order to effectively manage the credit exposure and minimize potential losses. The objective of this early warning system is to address potential problems while adequate options for action are still available. This early risk detection is a tenet of Deutsche Bank’s credit culture and is designed to raise management awareness of these positions. Credit limits are established by the Credit Risk Management function via the execution of assigned credit authorities. This also applies to settlement risk that must fall within limits pre-approved by Credit Risk Management considering risk appetite and in a manner that reflects expected settlement patterns for the subject counterparty. Credit approvals are documented by the signing of the credit report by the respective credit authority holders and are retained for future reference. Credit authority is generally assigned to individuals as personal credit authority according to the individual’s professional qualification and experience. All assigned credit authorities are reviewed on a periodic basis to help ensure that they are commensurate with the individual performance of the authority holder. Where an individual’s personal authority is insufficient to establish required credit limits, the transaction is referred to a higher credit authority holder or where necessary to an appropriate credit committee. Where personal and committee authorities are insufficient to establish appropriate limits, the case is referred to the Management Board for approval. Mitigation of credit risk on counterparty level In addition to determining counterparty credit quality and the alignment of the exposure with the bank’s concentration risk appetite, Deutsche Bank also uses various credit risk mitigation techniques to optimize credit exposure and reduce potential credit losses. Credit risk mitigants are applied in the following forms: – Comprehensive and enforceable credit documentation with adequate terms and conditions – Collateral in its various forms to reduce losses by increasing the recovery of obligations; key principles for collateral management include legal effectiveness and enforceability, prudent and realistic collateral valuations, risk and regulatory capital reduction, as well as cost efficiency – Risk transfers, which shift the risk of default of an obligor to a third-party including hedging executed by the bank’s Strategic Corporate Lending (SCL); other de-risking tools such as securitizations etc. may also be employed – Netting and collateral arrangements which reduce the credit exposure from derivatives and securities financing transactions (e.g. repo transactions) – Hedging of derivatives counterparty risk including CVA, using primarily CDS contracts via the bank’s Counterparty Portfolio Management desk Collateral Deutsche Bank regularly agrees on collateral to be received from customers that are subject to credit risk or to be provided by third parties agreed by legally effective and enforceable contracts as documented by a written and reasoned legal opinion. Collateral is credit protection in the form of (funded) assigned or pledged assets or (unfunded) third-party obligations that serves to mitigate the inherent risk of credit loss in an exposure, by either substituting the counterparty default risk or improving recoveries in the event of a default. Deutsche Bank generally takes all types of valuable and eligible collateral for its respective businesses but may limit accepted collateral types for specific businesses or regions as customary in the respective market or driven by purpose of efficiency. While collateral can be an alternative source of repayment, it does not replace the necessity of high-quality underwriting standards and a thorough assessment of the debt service ability of the counterparty in line with Article 194 (9) CRR. Deutsche Bank distinguishes following two types of collateral received: – Financial and other collateral, which enables Deutsche Bank to recover all or part of the outstanding exposure by liquidating the collateral asset provided, in cases where the counterparty is unable or unwilling to fulfill its primary obligations. Cash collateral, securities (equity, bonds), collateral pledges or assignments of other claims or inventory, movable assets (i.e., plant, machinery, ships and aircraft) and real estate typically fall into this category. All financial collateral is regularly, mostly daily, revalued and measured against the respective credit exposure. The value of other collateral, including real estate, is monitored based upon established processes that includes regular reviews or revaluations by internal and/or external experts – Guarantee collateral, which complements the counterparty’s ability to fulfill its obligation under the legal contract and as such is provided by uncorrelated third parties. Letters of credit, insurance contracts, export credit insurance, guarantees, credit derivatives and risk participations typically fall into this category. Guarantees and strong letters of comfort provided by correlated group members of customers (generally the parent company) are also accepted and used for risk transfer in approved rating scorecards. Guarantee collateral with a non-investment grade rating of the guarantor is limited Deutsche Bank’s processes seek to ensure that the collateral accepted for risk mitigation purposes is of high quality. This includes processes to generally ensure legally effective and enforceable documentation for realizable and measurable collateral assets which are evaluated within the on-boarding process by dedicated internal appraisers or teams with the respective qualification, skills and experience or adequate external valuers mandated in regulated processes. The applied valuations follow generally accepted valuation methods or models. Ongoing correctness of values is monitored by collateral type specific appropriate frequent and event-driven reviews considering relevant risk parameters. Revaluations are applied in cases of identified probable material deterioration and future monitoring may be adjusted respectively. The assessment of the suitability of collateral for a specific transaction is part of the credit decision and must be undertaken in a conservative way, including collateral haircuts that are applied. Deutsche Bank has collateral type specific haircuts in place which are regularly reviewed and approved. In this regard, Deutsche Bank strives to avoid “wrong-way” risk characteristics where the counterparty’s risk is positively correlated with the risk of deterioration in the collateral value. For guarantee collateral, the process for the analysis of the guarantor’s creditworthiness is aligned to the credit assessment process for counterparties. The valuation of collateral is considered under a liquidation scenario. The liquidation value is equal to the expected proceeds of collateral monetization/realization in a base case scenario, wherein a fair price is achieved through careful preparation and orderly liquidation of the collateral. Collateral can either move in value over time (dynamic value) or not (static value). The dynamic liquidation value generally includes a safety margin or haircut over realizable value to address liquidity and marketability aspects. The Group assigns a liquidation value to eligible collateral, based on, among other things: – The market value and / or lending value, notional amount or face value of a collateral as a starting point – The type of collateral; the currency mismatch, if any, between the secured exposure and the collateral; and a maturity mismatch, if any – The applicable legal environment or jurisdiction (onshore versus offshore collateral) – The market liquidity and volatility in relation to agreed termination clauses – The correlation between the performance of the borrower and the value of the collateral, e.g., in the case of the pledge of a borrower’s own shares or securities (in this case generally full correlation leads to no liquidation value) – The quality of physical collateral and potential for litigation or environmental risks; and – A determined collateral type specific haircut (0 – 100 %) reflecting collection risks (i.e. price risks over the average liquidation period and processing/utilization/sales costs) as specified in the respective policies Collateral haircut settings are typically based on available historic internal and/or external recovery data (expert opinions may also be used, where appropriate). They also incorporate a forward-looking component in the form of collection and valuation forecast provided by experts within Risk Management. Considering the expected proceeds from the liquidation of the different collateral types, respective value fluctuations, market specific liquidation costs and time applied haircuts vary between 0 to 100 %. When data is not sufficiently available or inconclusive, more conservative haircuts than otherwise used must be applied. Haircut settings are reviewed at least annually.

Managing and Mitigation of credit risk paragraph 2 [text block]

In order to reduce the credit risk resulting from OTC derivative transactions, where CCP clearing is not available, Deutsche Bank regularly seeks the execution of standard master agreements (such as master agreements for derivatives published by the International Swaps and Derivatives Association, Inc. (ISDA) or the German Master Agreement for Financial Derivative Transactions) with the bank’s counterparties. A master agreement allows for the close-out netting of rights and obligations arising under derivative transactions that have been entered into under such a master agreement upon the counterparty’s default, resulting in a single net claim owed by or to the counterparty. Payment netting may be agreed from time to time with the bank’s counterparties for multiple transactions having the same payment dates (e.g., foreign exchange transactions) pursuant to the terms of master agreements which can, reduce the bank’s settlement risk. In its risk measurement and risk assessment processes Deutsche Bank applies close-out netting only to the extent Deutsche Bank has concluded that the master agreement is legally valid and enforceable in all relevant jurisdictions and the recognition of close-out netting has been approved in accordance with the bank’s Netting Policies. Deutsche Bank also enters into credit support annexes (CSAs) to master agreements in order to further reduce the bank’s derivatives-related credit risk. These annexes generally provide risk mitigation through periodic, usually daily, margining of the covered exposure. The CSAs also provide for the right to terminate the related derivative transactions upon the counterparty’s failure to honor a margin call. As with netting, when Deutsche Bank believes the annex is enforceable, Deutsche Bank reflects this in its exposure measurement.

Managing and Mitigation of credit risk paragraph 3 [text block]

Concentrations within credit risk mitigation Concentrations within credit risk mitigations taken may occur if a number of guarantors and credit derivative providers with similar economic characteristics are engaged in comparable activities with changes in economic or industry conditions affecting their ability to meet contractual obligations. Concentration risk may also occur in collateral portfolios (e.g. multiple claims and receivables against third parties) which are considered conservatively within the valuation process and/or on-site inspections where applicable. Deutsche Bank uses a range of tools and metrics to monitor its credit risk mitigating activities and potential concentrations.

Managing and Mitigation of credit risk paragraph 4 [text block]

Managing credit risk on portfolio level

Enterprise Risk & Credit Risk Portfolio Management sets the framework for the management of concentration risks at a portfolio level. This includes strategically setting, monitoring, reviewing, reporting, and controlling credit risk appetites across various dimensions such as group, division, business unit, legal entity, branch, country, and industry level that need to be considered in the context of credit approvals. In addition, Enterprise Risk & Credit Risk Portfolio Management also provides a comprehensive and holistic view of the bank’s risk profile across risk types.

On a portfolio level, significant concentrations of credit risk could result from having material exposures to a number of counterparties with similar economic characteristics, or who are engaged in comparable activities, where these similarities may cause their ability to meet contractual obligations to be affected in the same manner by changes in economic or industry conditions.

Deutsche Bank’s portfolio management framework supports a comprehensive assessment of concentrations within its credit risk portfolio in order to keep concentrations within acceptable levels.

Risk and portfolio developments are regularly discussed at the

Credit Risk Appetite and Portfolio Management Forum which includes representation from across the Credit Risk Management function including the Head of Credit Risk Management.

Industry risk management

To manage industry risk, Deutsche Bank has grouped its corporate and financial institutions counterparties into various industry sub-portfolios. Portfolios are regularly reviewed with the frequency of review dependent on portfolio size and risk profile as well as risk developments. Larger / riskier portfolios are reviewed at least on an annual basis. Reviews highlight industry developments and risks to the bank’s credit portfolio, review cross-risk concentration risks, analyze the risk/reward profile of the portfolio and incorporate the results of an economic downside stress test. Finally, this analysis is used to define the credit strategies for the portfolio in question.

In the bank’s industry limit framework, thresholds are established for aggregate credit limits to counterparties within each industry sub-portfolio. For risk management purposes, the aggregation of limits across industry sectors follows an internal risk view that does not have to be congruent with NACE (Nomenclature des Activities Economiques dans la Communate Europeenne) code-based view applied elsewhere in this report. Regular industry portfolio overviews are prepared for the Enterprise Risk Committee to discuss recent developments and to agree on actions where necessary.

Beyond credit risk, the bank’s industry risk framework comprises of thresholds for Traded Credit Positions while key industry relevant non-financial risks are closely monitored.

Country risk management

Avoiding undue concentrations from a regional and country perspective is also an integral part of the bank’s credit risk management framework. In order to achieve this, country risk thresholds are applied to countries in Non-Japan Asia, Central Eastern Europe, Middle East & Africa and Latin America as well as selected Developed Markets countries (based on internal country risk ratings). Similar to industry risk, country portfolios are regularly reviewed with the frequency of review dependent on portfolio size and risk profile as well as risk developments. Larger/riskier portfolios are reviewed at least on an annual basis. These reviews assess amongst other factors, key macroeconomic and political risk developments and outlook; portfolio composition, quality and cross-risk concentrations under normal and stress conditions; analyze the risk/reward profile of the portfolio. Based on this and taking into account the Group’s Risk Appetite and strategy, country risk appetite and strategies are set.

In the bank’s country risk framework, thresholds are established for counterparty credit risk exposures in each country to manage the aggregate credit risk subject to country-specific economic and political events. These thresholds cover exposures to entities incorporated locally and subsidiaries of foreign multinational corporations as well as companies with significant economic or operational dependence on a specific country even though they are incorporated externally. In addition, gap risk thresholds are set to control the risk of loss due to intra-country wrong-way risk exposure. As such, for risk management purposes, the aggregation of exposures across countries follows an internal risk view that may differ from the geographical exposure view applied elsewhere in this report. Beyond credit risk, the bank’s country risk framework comprises thresholds for trading positions that measure the aggregate market value of traded credit risk positions. For Emerging Markets, thresholds are also set to measure the profit and loss impact under specific country stress scenarios on trading positions across the bank’s portfolio. Furthermore, thresholds are set for capital and intra-group funding exposure of Deutsche Bank entities in above countries given the transfer risk inherent in these cross-border positions. Key non-financial risks are closely monitored. Deutsche Bank’s country risk ratings represents a key tool in its management of country risk. They include:

• – Sovereign rating (set and managed by ERM): A measure of the probability of the sovereign defaulting on its foreign or local currency obligations
• – Transfer risk rating (set and managed by ERM): A measure of the probability of a “transfer risk event”, i.e., the risk that an otherwise solvent debtor is unable to meet its obligations due to inability to obtain foreign currency or to transfer assets as a result of direct sovereign intervention

All sovereign and transfer risk ratings are reviewed, at least on an annual basis.

Product/Asset class specific risk management

Complementary to the bank’s counterparty, industry and country risk approach, Deutsche Bank focuses on certain product/asset class specific risk concentrations and set limits or thresholds where required for risk management purposes. Specific risk limits are set in particular if a concentration of transactions of a specific type might lead to significant losses under certain conditions. In this respect, correlated losses might result from disruptions in the functioning of financial markets, significant moves in market parameters to which the respective product or asset class is sensitive to, or other risk drivers common to the asset class.

Underwriting of capital markets transactions

Specific focus is placed on transactions with underwriting risks where Deutsche Bank underwrites commitments with the intention to sell down or distribute part of the risk to third parties. These commitments include the undertaking to provide bank loans for syndication into the debt capital market and bridge loans for the issuance of notes. The inherent risks of being unsuccessful in the distribution of the facilities or the placement of the notes, comprise of a delayed distribution, funding of the underlying loans as well as a pricing risk as some underwriting commitments are additionally exposed to market risk in the form of widening credit spreads. Where applicable, Deutsche Bank dynamically hedges this credit spread risk to be within the approved market risk limit framework.

A major product, in which Deutsche Bank is active in underwriting, is leverage lending, which Deutsche Bank mainly executes through its Leveraged Debt Capital Markets business unit. The business model is a fee-based‚ originate to distribute approach focused on the distribution of largely unfunded underwriting commitments into the capital market. The afore mentioned risks regarding distribution and credit spread movement apply to this business unit, however, are managed under a range of specific notional as well as market risk limits. The latter require the business to also hedge its underwriting pipeline against market dislocations. The fee-based model of the bank’s Leveraged Debt Capital Markets business unit includes a restrictive approach to single-name risk concentrations retained on Deutsche Bank‘s balance sheet, which results in a diversified overall portfolio without any material concentrations. The resulting longer-term on-balance sheet portfolio is also subject to a comprehensive credit limit and hedging framework.

Deutsche Bank also assumes underwriting risk with respect to Commercial Real Estate loans, primarily in the Commercial Real Estate business unit in the Investment Bank where loans may be originated with the intent to securitize in the capital markets or syndicate to other lenders. The afore mentioned inherent underwriting risks such as delayed distribution and pricing risk are managed through notional caps, market risk limits and hedging against the risk of market dislocations.

In addition to underwriting risk, Deutsche Bank also focuses on concentration of transactions with specific risk dynamics (including risk to commercial real estate and risk from securitization positions).

In addition, the bank’s Private Bank and certain Corporate Bank businesses are managed via product-specific strategies setting the bank’s risk appetite for portfolios with similar credit risk characteristics, such as the retail portfolios of mortgages and consumer finance products as well as products for business clients. Here risk analyses are performed on portfolio level including further breakdown into business units as well as countries/regions. Analysis for individual clients is of secondary importance. In Wealth Management, target levels are set for global concentrations along products as well as based on type and liquidity of collateral.

Market Risk Management [Abstract]

Market Risk Framework [text block]

Market Risk Management Market Risk framework The vast majority of Deutsche Bank’s businesses are subject to market risk, defined as the potential for change in the market value of the Group’s trading and invested positions. Risk can arise from changes in interest rates, credit spreads, foreign exchange rates, equity prices, commodity prices and other relevant parameters, such as market volatility and market implied default probabilities. The market risk can affect accounting, economic and regulatory views of the exposure. Market Risk Management is part of Deutsche Bank’s independent Risk function and sits within the Market and Valuations Risk Management group. One of the primary objectives of Market Risk Management is to ensure that the business units’ risk exposure is within the approved risk appetite commensurate with its defined strategy. To achieve this objective, Market Risk Management works closely together with risk takers (“the business units”) and other control and support groups. The Group distinguishes between three substantially different types of market risk: – Trading market risk arises primarily through the market-making and client facilitation activities of the Investment Bank and Corporate Bank divisions. This involves taking positions in debt, equity, foreign exchange, other securities and commodities as well as in equivalent derivatives – Traded default risk arising from defaults and rating migrations relating to trading instruments – Non-trading market risk arises from market movements, primarily outside the activities of the trading units, in the banking book and from off-balance sheet items; this includes interest rate risk, credit spread risk, investment risk and foreign exchange risk as well as market risk arising from pension schemes, guaranteed funds and equity compensation; non-trading market risk also includes risk from the modeling of client deposits as well as savings and loan products Market Risk Management governance is designed and established to promote oversight of all market risks, effective decision-making and timely escalation to senior management. Market Risk Management defines and implements a framework to systematically identify, assess, monitor and report the Group’s market risk. Market risk managers identify market risks through active portfolio analysis and engagement with the business units.

Trading Market Risk [text block]

Trading Market Risk The Group’s primary mechanism to manage trading market risk is the application of risk appetite framework of which the limit framework is a key component. The Management Board, supported by Market Risk Management, sets group-wide value-at-risk, economic capital and portfolio stress testing limits for market risk in the trading book. Market Risk Management allocates this overall appetite to the Corporate Divisions and their individual business units based on established and agreed business plans. Deutsche Bank also has business aligned heads within Market Risk Management who establish business unit limits, by allocating the limit down to individual portfolios, geographical regions and types of market risks. Value-at-risk, economic capital and portfolio stress testing limits are used for managing all types of market risk at an overall portfolio level. As an additional and important complementary tool for managing certain portfolios or risk types, Market Risk Management performs risk analysis and business specific stress testing. Limits are also set on sensitivity and concentration/liquidity, exposure, business-level stress testing and event risk scenarios, taking into consideration business plans and the risk vs return assessment. Business units are responsible for adhering to the limits against which exposures are monitored and reported. The market risk limits set by Market Risk Management are monitored on a daily, weekly and monthly basis, dependent on the risk management tool being used.

Value-at-Risk [text block]

Internally developed Market Risk Models Value-at-Risk (VaR) VaR is a quantitative measure of the potential loss (in value) of Fair Value positions due to market movements that should not be exceeded in a defined period of time and with a defined confidence level. The Group’s value-at-risk for the trading businesses is based on internal model approach. In October 1998, the German Banking Supervisory Authority (now the BaFin) approved the bank’s internal model for calculating the regulatory market risk capital for general and specific market risks based on a sensitivity based Monte Carlo approach. In October 2020, the ECB approved a significant change to the VaR model, now a Historical Simulation approach predominantly utilizing full revaluation, although some portfolios remain on a sensitivity based approach. The new approach is used for both Risk Management and capital requirements. The new approach provides more accurate modelling of the risks, enhances the Group’s analysis capabilities and provides a more effective tool for risk management. Aside from enabling a more accurate view of market risk, the implementation of Historical Simulation VaR has brought about an even closer alignment of the market risk systems and models to the end of day pricing. Risk management VaR is calibrated to a 99 % confidence level and a one day holding period. This means we estimate there is a 1 in 100 chance that a mark-to-market loss from our trading positions will be at least as large as the reported VaR. For regulatory capital purposes, the VaR model is calibrated to a 99 % confidence interval and a ten day holding period. The calculation employs a Historical Simulation technique that uses one year of historical market data as input and observed correlations between the risk factors during this one year period. The VaR model is designed to take into account a comprehensive set of risk factors across all asset classes. Key risk factors are swap/government curves, index and issuer-specific credit curves, single equity and index prices, foreign exchange rates, commodity prices as well as their implied volatilities. To help ensure completeness in the risk coverage, second order risk factors, e.g. money market basis, implied dividends, option-adjusted spreads and precious metals lease rates are also considered in the VaR calculation. The list of risk factors include in the VaR model is reviewed regularly and enhanced as part of ongoing model performance reviews. The model incorporates both linear and, especially for derivatives, nonlinear impacts predominantly through a full revaluation approach but it also utilizes a sensitivity-based approach for certain portfolios. The full revaluation approach uses the historical changes to risk factors as input to pricing functions. Whilst this approach is computationally expensive, it does yield a more accurate view of market risk for nonlinear positions, especially under stressed scenarios. The sensitivity based approach uses sensitivities to underlying risk factors in combination with historical changes to those risk factors. For each business unit a separate VaR is calculated for each risk type, e.g. interest rate risk, credit spread risk, equity risk, foreign exchange risk and commodity risk. “Diversification effect” reflects the fact that the total VaR on a given day will be lower than the sum of the VaR relating to the individual risk types. Simply adding the VaR figures of the individual risk types to arrive at an aggregate VaR would imply the assumption that the losses in all risk types occur simultaneously. The VaR enables the Group to apply a consistent measure across the fair value exposures. It allows a comparison of risk in different businesses, and also provides a means of aggregating and netting positions within a portfolio to reflect correlations and offsets between different asset classes. Furthermore, it facilitates comparisons of the market risk both over time and against the daily trading results. When using VaR results a number of considerations should be taken into account. These include: – The use of historical market data may not be a good indicator of potential future events, particularly those that are extreme in nature; this “backward-looking” limitation can cause VaR to understate future potential losses (as in 2008), but can also cause it to be overstated immediately following a period of significant stress (as in COVID-19 pandemic) – The one day holding period does not fully capture the market risk arising during periods of illiquidity, when positions cannot be closed out or hedged within one day – VaR does not indicate the potential loss beyond the 99th quantile – Intra-day risk is not reflected in the end of day VaR calculation – There may be risks in the trading or banking book that are not fully captured in the VaR model (either partially captured or missing entirely)

Stressed Value-at-Risk [text block]

Stressed Value-at-Risk Stressed Value-at-Risk (SVaR) calculates a stressed value-at-risk measure based on a one year period of significant market stress. The Group calculates a stressed value-at-risk measure using a 99 % confidence level. Stressed VaR is calculated with a holding period of ten days. The SVaR calculation utilizes the same systems, trade information and processes as those used for the calculation of value-at-risk. The only difference is that historical market data and observed correlations from a period of significant financial stress (i.e., characterized by high volatilities) is used as an input for the Historical Simulation.

Incremental Risk Charge [text block]

Incremental Risk Charge Incremental Risk Charge captures default and credit rating migration risks for credit-sensitive positions in the trading book. The Group uses a Monte Carlo Simulation for calculating incremental risk charge as the 99.9 % quantile of the portfolio loss distribution over a one-year capital horizon under a constant position approach and for allocating contributory incremental risk charge to individual positions.

Nontrading Market Risk [text block]

Non-trading Market Risk Non-trading market risk arises primarily from activities outside of the trading units, in the banking book, including pension schemes and guarantees, and embedding considerations of different accounting treatments of transactions. Significant market risk factors the Group is exposed to and are overseen by risk management groups in that area are interest rate risk (including risk from embedded optionality and changes in behavioral patterns for certain product types), credit spread risk, foreign exchange risk (including structural foreign exchange risk), equity risk (including equity compensation related risk and investments in public and private equity as well as real estate, infrastructure and fund assets).

Nontrading Market Risk Economic Capital [text block]

Non-trading Market Risk Economic Capital Non-trading market risk economic capital is calculated either by applying the standard traded market risk EC methodology or through the use of non-traded market risk models that are specific to each risk class and which consider, among other factors, historically observed market moves, the liquidity of each asset class, and changes in client’s behavior in relation to products with behavioral optionalities.

Liquidity Risk Management [Abstract]

Liquidity Risk Management Framework [text block]

Liquidity risk management

Liquidity risk arises from DB Group’s potential inability to meet payment obligations when they come due or without incurring excessive costs. The Group’s liquidity risk management framework ensures that all necessary guidance and controls are established within the Group to fulfil its payment obligations at all times (including intraday) and to manage its liquidity and funding risks within the MB approved risk appetite, when executing the strategic plan. The framework considers relevant and significant drivers of liquidity risk, whether on-balance sheet or off-balance sheet.

Liquidity risk management framework

The Group’s liquidity risk management principles are documented in the global Liquidity Risk Management Policy” (LRMP) and the framework is described in the Liquidity Risk Management Framework” document. They adhere to the eight key risk management practices, namely risk governance, risk organization (3 LoD), risk culture, risk appetite and -strategy, risk identification and -assessment, risk mitigation and controls, risk measurement and reporting, stress planning and -execution. All additional policies and procedures (both global and local) issued by the liquidity risk management functions further define the requirements specific to liquidity risk practices. They are subordinate to the LRMP and are subject to the standards the LRMP sets forth. The liquidity managing functions are organized in alignment with the three lines of defense structure, which is described in the Risk Management Policy”. The lines of business and Treasury comprise the 1LoD is responsible for executing the steps needed to manage the bank’s liquidity position. Risk comprises the 2LoD, responsible for providing independent risk oversight, challenge, and validation of activities conducted by the 1LoD including establishing the risk appetite and Group level control standards. Group Audit comprises the 3LoD, responsible for overseeing the activities of both the 1LoD and 2LoD. The individual roles and responsibilities within the liquidity risk management framework are laid out and documented in the global responsibility matrix, which provides further clarity and transparency across all involved stakeholders.

In accordance with the ECB’s SREP (and revised ILAAP requirement issued in November 2018), the Group has implemented an Internal Liquidity Adequacy Assessment Process (ILAAP), which is reviewed at least annually and approved by the MB. Liquidity Risk Management undertakes ongoing oversight on activities conducted within the mandate of Treasury Liquidity Management to most effectively manage the liquidity of the Group and steer business activities, while ensuring the bank’s risk appetite is adhered to. The Internal Liquidity Adequacy Assessment Process provides comprehensive documentation and assessment of the bank’s liquidity risk management framework, which includes the identification of key liquidity and funding risks to which the Group is exposed; describing how these risks are identified, monitored and measured; and describing the techniques and resources used to manage and mitigate these risks.

The MB defines the liquidity and funding risk strategy for the Group as well as the risk appetite, based on recommendations made by the Group Asset and Liability Committee (ALCO) and Group Risk Committee. The MB reviews and approves the risk appetite at least annually. The risk appetite is applied to the Group and its key liquidity entities e.g., DB AG to monitor and control liquidity risk as well as the Group’s long-term funding and issuance plan.

The Group’s liquidity risk appetite, which is defined through qualitative principles and supporting quantitative metrics, is laid out in the Risk Appetite Statement” and is subject to the standards defined in the Risk Appetite Policy”. This Risk Appetite Statement is further underpinned by the liquidity risk controls framework consisting of risk appetite limits, as well as a suite of non-risk appetite limits, thresholds and early warning indicators, which are defined in the Liquidity Risk Controls Policy”.

Deutsche Bank implemented a dedicated stress testing and risk appetite Framework defined by Liquidity Risk Management, which ensures its liquidity position is balanced across the Group, its KLEs and across currencies.

Treasury manages liquidity and funding, in accordance with the MB-approved risk appetite across a range of relevant metrics and implements several tools including business level limits, to ensure compliance. As such, Treasury works closely with Liquidity Risk Management and the business divisions to identify, analyze and monitor underlying liquidity risk characteristics within business portfolios. These parties are engaged in regular dialogue regarding changes in the Group’s liquidity position arising from business activities and market circumstances.

Furthermore, the Group ensures at the level of each liquidity relevant entity that all local liquidity metrics are managed in compliance with the defined risk appetite. Local liquidity surpluses are pooled in DB AG hubs and local liquidity shortfalls can be met through support from DB AG hubs. Transfers of liquidity capacity between entities are subject to the approval framework outlined in the Intercompany Funding Policy” involving the Group’s liquidity steering function as well as the local liquidity managers considering the compliance of metrics like LCR, NSFR (Pillar 1) and sNLP (Pillar 2). Available surplus that resides in entities with restriction to transfer liquidity to other Group entities, for example due to regulatory lending requirements, is treated as trapped and as such not considered in the calculation of the consolidated group liquidity surplus.

The MB is informed about the Group’s performance against the key liquidity metrics, including the risk appetite and internal and market indicators, via a weekly liquidity dashboard. Liquidity & Treasury Reporting & Analysis (LTRA) has overall accountability for the accurate and timely production of both external regulatory liquidity reporting (Pillar 1) as well as internal management reporting (Pillar 2) for liquidity risk of the Group. In addition, LTRA is responsible for the development of management information systems and the related analysis to support the liquidity risk framework and its governance for Treasury and LRM.

As part of the annual strategic planning process, Treasury projects the development of the key liquidity and funding metrics including the USD currency exposure based on anticipated business activities to ensure that the strategic plan can be executed in accordance with the Group’s risk appetite.

Deutsche Bank has a wide range of funding sources, including retail and institutional deposits, unsecured and secured wholesale funding, as well as debt issuance in the capital markets. Group ALCo is the Group’s decisive governing body mandated by the MB to optimize the sourcing and deployment of the Group’s balance sheet and financial resources in line with the MB’s risk appetite and strategy. The Group ALCo has the overarching responsibility to define, approve and optimize the Group’s funding strategy.

Deutsche Bank’s Group Contingency Funding Plan outlines, how the Group would respond to an actual or anticipated liquidity stress event. It includes a decisive set of actions that can be taken to raise cash and recover the Group’s key liquidity metrics in times of liquidity stress. The Contingency Funding Plan includes a clear governance structure and well-defined liquidity risk indicators to ensure timely escalation and effective decision-making, communication, and coordination during a liquidity stress event. Deutsche Bank has established the Financial Resource Management Council, which is responsible for oversight of capital and liquidity across contingency, recovery, and resolution scenarios in a defined crisis situation.

Short-term liquidity and wholesale funding [text block]

Short-term liquidity and wholesale funding The Group tracks all contractual cash flows from wholesale funding sources on a daily basis, over a twelve-month horizon. For this purpose, the Group considers wholesale funding to include unsecured liabilities largely raised by Treasury Markets Pool, as well as secured liabilities primarily raised by the Investment Bank division. Wholesale funding counterparties typically include corporates, banks and other financial institutions, governments, and sovereigns. The Group has implemented a set of limits and thresholds to restrict its exposure to wholesale counterparties, which have historically demonstrated the most susceptibility to market stress. Wholesale funding limits are monitored daily and apply to the total outstanding volume of wholesale funding across all currencies, for both secured and unsecured funding with specific tenor limits. Liquidity reserves constitute the primary mitigant against potential stress in the short-term. The tables in the section Liquidity Risk Exposure: Funding Diversification” show the contractual maturity of the Group’s short-term wholesale funding and capital markets issuance.

Liquidity stress testing and scenario analysis [text block]

Liquidity stress testing and scenario analysis Global internal liquidity stress testing and scenario analysis is used for measuring liquidity risk and evaluating the Group’s short-term liquidity position within the liquidity framework. This complements the daily operational cash management process. The long-term liquidity strategy based on contractual and behavioral modelled cash flow information is represented by a long-term metric known as the Funding Matrix (refer to Funding Risk Management below). The global liquidity stress testing process is managed by Treasury in accordance with the MB approved risk appetite. Treasury is responsible for the design of the overall methodology, the choice of liquidity risk drivers and the determination of appropriate assumptions (parameters) to translate input data into stress testing output. Liquidity Risk Management is responsible for the definition of the stress scenarios. Under the principles and policy requirements laid out by Model Risk Management, Liquidity Risk Management and Model Risk Management perform the independent validation of liquidity risk models and non-model estimates. LTRA is responsible for implementing these methodologies and performing the stress test calculation in conjunction with Treasury, Liquidity Risk Management and IT. Stress testing and scenario analysis are used to describe and evaluate the impact of sudden and severe stress events on the Group’s liquidity position. Deutsche Bank has selected four scenarios to calculate the Group’s stressed Net Liquidity Position (sNLP”). These scenarios are designed to capture potential outcomes which may be experienced by the Group. The most severe scenario assesses the potential consequences of a combined market-wide and idiosyncratic stress event, including downgrades of our credit rating. Under each of the scenarios, the impact of a liquidity stress event over different time horizons and across multiple liquidity risk drivers, covering all business lines and product areas is considered. The output from this scenario analysis feeds the Group Wide Stress Test, which considers the impact of scenarios across all risk stripes. In addition, potential funding requirements from contingent liquidity risks which can arise under stress, including drawdowns on facilities, increased collateral requirements under derivative agreements, and outflows from deposits with a contractual rating linked trigger are included in the analysis. Subsequently, countermeasures, which are the actions the Group would take to counterbalance the outflows incurred during a stress event, are taken into consideration. Those countermeasures include the usage of the Group’s Liquidity Reserve and generating liquidity from other unencumbered, marketable assets without causing any material impact on the Group’s business model. Stress testing is conducted at a global level and for defined entities relevant for liquidity risk management. The stress analysis covers an eight-week stress horizon which is considered to be the most critical time span during a liquidity crisis requiring that liquidity is actively assessed and steered on a Group level. In addition to the consolidated currency stress test, further stress tests are performed for material currencies (EUR, USD and GBP). On a global level and in the U.S. liquidity stress tests a twelve-months period is covered. Additionally, stress test results are monitored over a twelve-month period with specific risk limits, if required by local regulators. Ad-hoc analysis may be conducted to reflect the impact of potential downside events that could affect the Group such as climate / ESG-related events. Relevant stress assumptions are applied to reflect liquidity flows from risk drivers and on-balance sheet and off-balance sheet products. The suite of stress testing scenarios and assumptions are reviewed on a regular basis and are updated when enhancements are made to stress testing methodologies. Complementing the daily liquidity stress testing, the Group also conducts regular group-wide stress tests run by Enterprise Risk Management, which analyze liquidity risks in conjunction with the other defined risk types and evaluate their impact and interplay to both capital and liquidity positions as described in Risk and Capital Framework Stress testing. The tables in the section Liquidity Risk Exposure: Stress Testing and Scenario Analysis” show the results of the internal global liquidity stress test under the various scenarios.

Funding Risk Management [text block]

Funding Risk Management and Funding Diversification

In line with regulatory guidelines, Deutsche Bank has developed a set of internal indicators to measure its inherent funding risks. These are considered for steering purposes in addition to the regulatory metric Net Stable Funding Ratio (NSFR).

The Group’s primary internal tool for monitoring and managing structural funding risk is the Funding Matrix. The Funding Matrix assesses the Group’s structural funding profile over a time horizon beyond one year. To produce the Funding Matrix, all funding-relevant assets and liabilities are mapped into time buckets corresponding to their contractual or modeled maturities. This allows the Group to identify expected excesses and shortfalls in term liabilities over assets in each time bucket, facilitating the management of potential liquidity exposures.

The liquidity profile is based on contractual cash flow information. If the contractual maturity profile of a product does not adequately reflect the liquidity profile, it is replaced by modeling assumptions. Short-term balance sheet items (<1yr) or matched funded structures (asset and liabilities directly matched with no liquidity risk) are excluded from the term analysis.

The bottom-up assessment by individual business line is combined with a top-down reconciliation against the Group’s IFRS balance sheet. From the cumulative term profile of assets and liabilities beyond 1 year, long-funded surpluses or short-funded gaps in the Group’s maturity structure can be identified. The cumulative profile is thereby built up starting from the greater than ten-year” bucket down to the greater than one-year” bucket. The Funding Matrix is also undertaken for material foreign currencies (USD and GBP).

The Group relies on a vast range of funding sources. These protect its liquidity position twofold. Firstly, since stress events may impact funding markets differently, maintaining a well-diversified funding portfolio will lower the average impact for the Group. Secondly, when experiencing liquidity stress, having access to a wide range of funding sources significantly improves the Group’s ability to tap different funding markets. The diversification across products is complemented by explicit thresholds for selected, less stable products. Additionally, insufficient counterparty diversification can adversely impact the stability of the Group’s liquidity and funding position, in particular when those funds have a shorter duration. As such, Treasury monitors the development of the Group’s top funding counterparties.

The stability of DB Group’s funding position can be negatively impacted by various forms of industry risks. These are typically medium to long term structural trends with potentially significant long-term impact on the economy and consequently on banks’ balance sheets. DB is performing ad-hoc analyses on such emerging risks to assess the impact of such trends on its funding position to ensure that mitigating measures will be taken well in time when deemed necessary. In addition, Treasury evaluates current market access information in its significant funding markets on a regular basis. Market access information is compiled quarterly and presented to Group ALCo.

To diversify our refinancing activities, Deutsche Bank holds a license to issue mortgage Pfandbriefe and maintains a program to issue structured covered bonds. Additionally, the Group continues to run a program for the purpose of issuing covered bonds under Spanish law (Cedulas). The Group has also participated in ECB’s TLTRO III program. Additionally, the Group expanded its potential investor base through the introduction of its Green Bond framework in 2020. Following the inaugural green issuances in 2020, the Group has continuously expanded its Green Bond issuance activity. Furthermore, multiple green structured notes, first green deposits and first green repurchase agreements (repos) were executed. Various teams within DB continue to work on expanding the Group’s green footprint on the asset as well the liability side.

The chart Liquidity Risk Exposure: Funding Diversification” shows the composition of external funding sources that contribute to the liquidity risk position, both in EUR billion and as a percentage of our total external funding sources.

Liquidity reserves [text block]

Liquidity Reserves Liquidity Reserves comprise available cash and cash equivalents, unencumbered highly liquid securities (including government and agency bonds and government guarantees) and other unencumbered central bank eligible assets. Certain intraday requirements and mandatory minimum reserves are directly deducted in the calculation of the Liquidity Reserves while other intraday outflows are represented in the Group’s internal liquidity model.

Disclosure of Risk Concentration and Risk Diversification [text block]

Portfolio concentration risk Risk concentrations refer to clusters of the same or similar risk drivers within specific risk types (intra-risk concentrations in credit, market, operational and strategic risks) as well as across different risk types (inter-risk concentrations). They occur within and across counterparties, businesses, regions/countries, industries and products. The management and monitoring of risk concentrations is achieved through a quantitative and qualitative approach, as follows: – Intra-risk concentrations are assessed, monitored and mitigated by the individual risk functions (enterprise, credit, market, operational, liquidity and strategic risk management). This is supported by limit setting on different levels and/or management according to each risk type – Inter-risk concentrations are managed through quantitative top-down stress-testing and qualitative bottom-up reviews, identifying and assessing risk themes independent of any risk type and providing a holistic view across the bank. The diversification effects between credit, market, operational and strategic risk are measured through a dedicated risk model that quantifies the diversification benefit caused by non-perfect correlations between these risk types. The calculation of the risk type diversification benefit is intended to ensure that the standalone economic capital figures for the individual risk types are aggregated in an economically meaningful way The most senior governance body for the oversight of risk concentrations throughout 2022 was the Group Risk Committee (GRC).

Environmental, social and governance risk [text block]

Environmental, social and governance risk The impacts of rising global temperatures, the enhanced focus on climate change and the transition to a net-zero economy from society, regulators and the banking sector have led to the emergence of new and increasing sources of financial and non-financial risks. These include the physical risks arising from extreme weather events, which are growing in frequency and severity, as well as transition risks as carbon intensive sectors are faced with higher taxation, reduced demand and potentially restricted access to financing. These risks can impact Deutsche Bank across a broad range of financial and non-financial risk types. Financial institutions are facing increased scrutiny on climate and broader ESG-related issues from governments, regulators, shareholders and other bodies, leading to reputational risks if the Group is not seen to support the transition to a lower carbon economy, to protect biodiversity and human rights. Deutsche Bank is reviewing and enhancing its ESG risk management frameworks in alignment with regulatory guidance and to ensure that we actively manage ESG risks and prevent greenwashing. There is a lack of consistent and comprehensive ESG data and methodologies available today which means that the bank is heavily reliant on proxy estimates and qualitative approaches when assessing these risks and introduce a high degree of uncertainty into climate-related disclosures. Deutsche Bank is committed to managing business activities and operations in a sustainable manner, including aligning its portfolios with net zero emissions by 2050. In October 2022 Deutsche Bank announced net zero aligned interim (2030) and final (2050) targets for four key carbon intensive sectors: Oil and Gas (upstream), Power Generation, Automotives (light duty vehicles) and Steel. Targets are fully embedded into internal risk management frameworks and processes. Deutsche Bank’s Group Sustainability Committee, which is chaired by the Chief Executive Officer, decides on all important sustainability initiatives. In 2022 the bank appointed its first Chief Sustainability Officer and established a Sustainability Steering Committee responsible for ESG transformation management and oversight. The Group Risk Committee, chaired by the Chief Risk Officer, is established by the Management Board to serve as the central forum for review and decision making on matters related to risk, capital, and liquidity. This includes the responsibility for developing the bank’s Climate and broader ESG Risk Frameworks. A dedicated ESG Risk Forum oversees the integration of ESG risks into the bank’s existing financial and non-financial risk management frameworks. Deutsche Bank’s business activities are governed by a dedicated Climate and Environmental Risk Policy outlining roles, responsibilities as well as qualitative risk appetite principles and quantitative risk-appetite thresholds and KPIs. In addition, the bank’s Environmental and Social policy outlines specific restrictions for certain sectors. Deutsche Bank uses a number of complementary tools to identify and assess risks including the Group’s risk identification process, an internal climate risk taxonomy and regular internal reporting of portfolio financed emissions and intensities and progress against net zero targets.