Risk Report - Risk Management

12 Months Ended

Dec. 31, 2023

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 first and second line of defense, 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 Group risk inventory are reported to the Enterprise Risk Committee for review and approval. The inventory is also discussed in the Group Risk Committee and reported to the Management Board. The inventory informs key risk management processes, including 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 Group risk type taxonomy, where a corresponding materiality assessment is also provided.

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. It captures the risk of loss because of a deterioration of a counterparty’s creditworthiness or the failure of a counterparty to meet the terms of any contract with Deutsche Bank or otherwise perform as agreed.

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 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 is only accepted:

– for adopted clients

– after completed appropriate due diligence led by the respective origination teams as 1st LoD

– New products and changes to existing products have to be assessed within DB Group’s new product approval (NPA) framework

– If a Rating has been assigned in line with agreed and approved processes

– If all credit relevant exposures are correctly reflected in the relevant risk systems

– If plans for an orderly termination of the risk positions have been considered

– Credit Risk is assumed within the applicable Risk Appetite limits set for divisions, countries, industries, etc.

– P&L responsibility for credit exposures is owned by the originating Group Division

– Risk taken needs to be adequately compensated

– Risk must be continuously monitored and managed across 1st and 2nd LoD

– Credit standards are applied consistently across all Group Divisions in order to maintain a favorable risk profile in line with the Risk Appetite

– Collateral or other risk mitigating, hedging or rating transfer instruments, which can be an alternative source of repayment do not substitute for underwriting standards and a thorough assessment of the debt service ability of a counterparty has to be performed during the credit process; essentially rating transfer instruments are supporting documents (e.g., comfort letters or parental guarantees) helping the debtor to prevent a default; consequently, this effect can be considered in the Probability of Default

– Deutsche Bank strives to adequately secure, guarantee or hedge outright cash risk and longer tenor-exposures; this approach does usually not include lower risk short-term transactions and facilities supporting specific trade finance or other lower risk products where the margin allows for adequate loss coverage

– Deutsche Bank measures and consolidates globally all exposure and facilities to the same Obligor; a Key Contact Person (KCP) within a Credit Team is assigned to each group of connected clients (Obligor) to globally co-ordinate the Credit Risk process for the respective obligor

– Deutsche Bank has established within Credit Risk Management – where appropriate – specialized teams for deriving internal client ratings, analyzing and approving transactions or covering workout clients; for transaction approval purposes, structured credit risk management teams are aligned to the respective products or specific risks to ascertain adequate product expertise

– Where required, Deutsche Bank has established processes to manage credit exposures at a legal entity or regional level

– To meet the requirements of Article 190 CRR, Deutsche Bank has allocated the various control requirements for the credit processes to 2nd LoD units best suited to perform such controls

Measuring Credit Risk [abstract]

Measuring credit risk paragraph 1 [text block]

Measuring Credit Risk Credit risk is measured by credit rating, regulatory and internal capital demand and other key components like credit limits as 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 an individual counterparty 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 supported by ongoing monitoring of counterparties. 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. Specific rating approaches have been established to best reflect the respective characteristics of exposure classes, including specific product types, central governments and central banks, institutions, corporates and retail. Counterparties in the bank’s non-retail portfolios are rated by Deutsche Bank’s independent Credit Risk Management function partly using automated Rating systems. Given the largely homogeneous nature of the retail portfolio, counterparty creditworthiness and ratings are predominately derived by utilizing an automated decision engine. Country risk-related ratings are provided by Enterprise Risk Management (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, as a key component for managing the bank’s credit portfolio, including individual 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 above others 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 appropriate 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.

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 disclosure provides a description of the Group‘s IFRS 9 model and methodology, changes implemented in 2023 as well as key model assumptions. This chapter also highlights novel risks in focus in 2023 and how Deutsche Bank assessed impact of these latest developments on its ECL calculations, and in particular, how the model properly takes into account the impacts of the uncertainties noted in 2023 and at year end, along with the impact from reasonable changes in the Group’s key model assumptions.

These credit risk management activities and assessments are embedded in the bank’s overall control and governance framework for credit risk which includes the estimation of expected credit losses under IFRS 9 and the governance around the models used. 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. Further explanations are provided regarding management overlays applied to the credit loss allowance, how reviews of relevant assumptions and inputs to the ECL calculation are performed and how as part of the model reviews, potential model imprecision and whether any overlays were necessary, are assessed. The Group also presents background on management overlays recorded at the end of 2022, throughout 2023 and at the end of 2023. To provide additional transparency on the impact of reasonable changes to the key assumptions, model sensitivities are presented in a separate section which 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 and the Group purchased at a discount.

The IFRS 9 impairment approach is an integral part of the Group’s credit risk management procedures. The estimation of expected credit loss (ECL) 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 order to calculate lifetime ECL, the Group’s calculation derives the corresponding lifetime PDs from migration matrices that reflect economic forecasts.

In July 2023, the Group completed the migration of Postbank clients into the IT systems of Deutsche Bank, which resulted in a Group-wide alignment of the IFRS 9 impairment model and methodologies, while specific models previously applied for Postbank were decommissioned. This change in estimate resulted in an immaterial net increase in the Group’s allowance for credit losses.

In September 2023, the Group received approval from authorities to align the Probability of Default (PD) model for large corporates and leveraged lending with regulatory requirements. The model is used for the determination of capital demand under the Basel Internal Ratings Based Approach, for internal risk management practices and is leveraged for IFRS 9 ECL purposes. The related change in estimate reduced the Group’s ECL as of September 30, 2023, by € 101 million.

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 transfer of financial assets to Stage 3 is based on the status of the borrower being in default. If a borrower is in default, 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 is deemed to have 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 reviewed 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 the Group’s watchlist, being transferred to workout status, payments being 30 days or more past due or being in forbearance. As long as the condition 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 two 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 three months or one 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 the homogeneous and non-homogenous portfolios, as well as 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 to a large extent 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 factor 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 factor 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 modelled for some portfolios. 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 quarterly 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, 2023, the consensus data applied in the ECL model was deemed to have reflected the latest macroeconomic developments and after considering all relevant uncertainties in the MEVs 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, 2023, and as of December 31, 2022.

Forward-looking information applied

	December 31, 2023¹ ²
	Year 1(4 quarter avg)	Year 2(4 quarter avg)
Commodity - Gold	1,957.34	1,958.16
Commodity - WTI	82.52	83.56
Credit - CDX Emerging Markets	195.16	192.83
Credit - CDX High Yield	451.57	466.40
Credit - CDX IG	70.04	72.12
Credit - High Yield Index	4.05	4.19
Credit - ITX Europe 125	73.09	72.21
Equity - MSCI Asia	1,293	1,297
Equity - Nikkei	33,188	34,051
Equity - S&P500	4,514	4,621
GDP - Developing Asia	4.94%	4.37%
GDP - Emerging Markets	4.08%	4.01%
GDP - Eurozone	0.13%	1.08%
GDP - Germany	0.12%	1.30%
GDP - Italy	0.33%	1.03%
GDP - USA	1.75%	1.31%
Real Estate Prices - US CRE Index	353.41	347.99
Unemployment - Eurozone	6.67%	6.64%
Unemployment - Germany	3.12%	3.13%
Unemployment - Italy	7.75%	7.68%
Unemployment - Japan	2.58%	2.42%
Unemployment - Spain	11.96%	11.67%
Unemployment - USA	4.19%	4.40%

¹ MEV as of December 6, 2023, which barely changed until December 29, 2023

² Year 1 equals fourth quarter of 2023 to third quarter of 2024, Year 2 equals fourth quarter of 2024 to third quarter of 2025

	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

Focus areas in 2023

Deutsche Bank’s macroeconomic environment in 2023 was characterized by the persistently high inflation which drove further monetary tightening, risks of a higher-for-longer interest rate environment and generally tighter financial conditions in the major advanced economies. Towards the end of the year, evidence of waning inflation pressure led to a repricing of market expectations with respect to earlier and more significant central bank rate cuts in 2024 accompanied by an easing of financial conditions. In addition, there were episodes of significant market volatility particularly around selective failures and/or restructurings in the U.S. and European banking sector as well as increasing pressure on the Commercial Real Estate (CRE) market, particularly in the U.S. office sector.

To ensure that Deutsche Bank’s ECL model was taking into account the uncertainties in the macroeconomic environment throughout 2023, the Group reviewed emerging risks to assess its potential downside and to manage the bank’s credit strategy and risk appetite on an ongoing basis. Overall, Deutsche Bank believes the actions taken as a result of these reviews were designated to ensure the bank was adequately provisioned for its expected credit losses as of December 31, 2023.

Commercial Real Estate

CRE markets continue to face headwinds due to the impacts of higher interest rates, decreasing market liquidity combined with tightened lending conditions, and structural changes in the office sector.

The market stress has been more pronounced in the U.S. where property price indices point to a greater decline of CRE asset values from recent peaks compared to Europe and APAC. Similarly, within the office segment, the market weakness is most evident in the U.S., reflected in subdued leasing activity and higher vacancy rates compared to Europe.

In the current environment, the main risk for the portfolio is refinancing risk. CRE loans often have a significant portion of their principal amount payable at maturity. Under current market conditions, borrowers may have difficulty obtaining a new loan to repay the maturing debt or fulfil conditions to extend the loan. The Group is closely monitoring its CRE portfolio for indications of elevated refinancing risk.

Amidst interest rate hikes commencing in 2022 and real estate market stress increasing, the Group has proactively worked with borrowers to address upcoming maturities to establish terms for loan amendments and extensions (e.g. extensions of terms) which in many cases, are classified as forbearance triggering Stage 2 classification under IFRS, but are not always modifications according under IFRS. However, in certain cases, no agreement can be reached on loan extensions or loan amendments, and the borrower’s inability to obtain refinancing leads to a default. This has resulted in higher Stage 3 ECL for 2023 compared to 2022, and there is continued uncertainty with respect to future defaults and the timing of a recovery in the CRE markets.

The CRE portfolio consists of lending arrangements originated across various parts of the Group and client segments. The Group’s CRE portfolio under the Group’s CRE definition includes exposures reported under the Main Credit Exposure Categories by Industry Sectors for Real Estate Activities NACE but also exposures reported under other NACE classifications including Financial and Insurance Activities.

Within the CRE portfolio, the Group differentiates between recourse and non-recourse financing. Recourse CRE financings typically have a lower inherent risk profile based on recourse to creditworthy entities or individuals, in addition to mortgage collateral. Recourse CRE exposures range from secured recourse lending for business or commercial properties to property companies, Wealth Management clients, as well as other private and corporate clients.

Non-recourse financings rely on sources of repayment that are typically limited to the cash flows generated by the financed property and the ability to refinance such loans may be constrained by the underlying property value and income stream generated by such property at the time of refinancing.

The entire CRE loan portfolio is subject to periodic stress testing under Deutsche Bank’s Group Wide Stress Test Framework. In addition, Deutsche Bank uses bespoke portfolio stress testing for certain sub-segments of the CRE loan portfolio to obtain a more comprehensive view of potential downside risks. For the year ending December 31, 2023, the Group performed a bespoke portfolio stress test on a subset of the non-recourse financing portfolio deemed higher risk based on its heightened sensitivity to current CRE market stress factors, including higher interest rates, declining collateral values and elevated refinancing risk due to loan structures with a high proportion of their outstanding principal balance payable at maturity.

As of December 31, 2023, the non-recourse portfolio subject to bespoke portfolio stress testing, also referred to as the higher risk CRE portfolio or the stress-tested CRE portfolio, amounted to € 31.2 billion of the € 38.2 billion non-recourse CRE portfolio, excluding only sub-portfolios with less impacted risk drivers such as data centers and municipal social housing, which benefit from strong underlying demand fundamentals. The reduction in the non-recourse CRE portfolio and stress-tested CRE portfolio since December 31, 2022 was € 0.7 billion and € 1.2 billion, respectively, mainly driven by paydowns and loan sales. The reduction in the non-recourse CRE portfolio in comparison to the stress-tested CRE portfolio was lower, as selective originations continued in the aforementioned lower risk non-recourse CRE sub-portfolios such as data centers and municipal social housing.

The following table provides an overview of the Group’s Real Estate Activities and other industry sectors (NACE) contributing to Deutsche Bank’s non-recourse and stress-tested CRE portfolio as of December 31, 2023, and December 31, 2022, respectively.

Overview of CRE portfolio

	Dec 31, 2023		Dec 31, 2022
in € m.	Gross Carrying Amount¹	Allowance for Credit Losses²	Gross Carrying Amount¹	Allowance for Credit Losses²
Real Estate Activities³	49,267	460	47,973	236
thereof: non-recourse	25,073	382	24,832	147
thereof: stress-tested portfolio	21,331	364	22,144	135
Other industry sectors³ non-recourse	13,119	225	14,037	189
thereof: stress-tested portfolio	9,879	114	10,242	101
Total non-recourse CRE portfolio	38,192	606	38,869	336
thereof: stress-tested portfolio	31,210	478	32,386	236

¹ Loans at amortized cost

² Allowance for credit losses do not include allowance for country risk

³ Industry sector by NACE (Nomenclature des Activités Économiques dans la Communauté Européenne) code

The following table shows the non-recourse CRE portfolio by IFRS 9 stages as well as provision for credit losses recorded as of December 31, 2023, and December 31, 2022.

Non-recourse CRE portfolio

	Dec 31, 2023	Dec 31, 2022
in € m.	Gross Carrying Amount¹	Gross Carrying Amount¹
Exposure by stages
Stage 1	27,325	31,892
Stage 2	7,661	5,233
Stage 3	3,206	1,744
Total	38,192	38,869
	2023	2022
Provision for Credit Losses²	445	128

¹ Loans at amortized cost

² Provision for Credit Losses do not include country risk provisions

The year on year increase in Stage 2 and Stage 3 exposures is reflective of the deterioration in CRE markets leading to increased loans added to the watchlist and forbearance measures as well as increasing defaults.

The following table shows the stress-tested CRE portfolio by IFRS 9 stages, region, property type and average weighted loan to value (LTV) as well as provision for credit losses recorded for the year ended December 31, 2023, and December 31, 2022, respectively.

Stress-tested CRE portfolio

	Dec 31, 2023	Dec 31, 2022
in € m.	Gross Carrying Amount¹	Gross Carrying Amount¹
Exposure by stages
Stage 1	21,568	26,199
Stage 2	6,889	5,046
Stage 3	2,753	1,141
Total	31,210	32,386
thereof:
North America	56%	57%
Western Europe (including Germany)	36%2	36%
Asia/Pacific	7%	7%
thereof: offices	42%	41%
North America	23%	22%
Western Europe (including Germany)	17%3	15%
Asia/Pacific	2%	3%
thereof: residential	14%	12%
thereof: hospitality	10%	11%
thereof: retail	9%	9%
Weighted average LTV, in %
Investment Bank	66%	62%
Corporate Bank	53%	53%
Other Business	68%	56%
	2023	2022
Provision for Credit Losses4	388	103
thereof: North America	298	87

¹ Loans at amortized cost

² Germany accounts for ca 7 % of the total stress-tested CRE portfolio

³ Office loans in Germany account for 4 % of total office loans in the stress-tested CRE portfolio

⁴ Provision for Credit Losses do not include country risk provisions

The average LTV in the U.S. office loan segment was 81 % as of December 31, 2023, versus 60 % as of December 31, 2022. LTV calculations are based on latest externally appraised values which are additionally subject to regular interim internal adjustments. While the Group is updating CRE collateral values where applicable, such values and their underlying assumptions are subject to a higher degree of fluctuation and uncertainty in the current environment of heightened market volatility and reduced market liquidity. A continuation of the current stressed market conditions could have a further adverse impact on commercial real estate property values and LTV ratios.

Stage classification and provisioning levels are primarily based on the Group’s assessment of a borrower’s ability to generate recurring cash flows, its ability to obtain refinancing at the loan’s maturity, and an assessment of the financed property’s collateral value. Deutsche Bank actively monitors these factors for potential signs of deterioration to ensure timely adjustment of the borrower’s loan classifications. When a loan is deemed to be impaired, the Group calculates required credit loss provisions using multiple potential scenarios for loan resolution, weighted by their expected probabilities and taking into account information available at that point. Such assessments are inherently subjective with respect to scenario weightings and subject to various assumptions, including future cash flows generated by a property and potential property liquidation proceeds. These assumptions are subject to uncertainties which are exacerbated in the current volatile market environment such that deviating developments to initial assumptions could have a material future impact on calculated provisions. Additional uncertainty exists within the office sector due to the uncertain long-term impact of remote working arrangements on demand for office space.

Given the near-term outlook for interest rates, the Group expects stressed market conditions to continue into 2024 which could result in further deterioration of asset quality and higher credit loss provisions, which is reflected in the communicated guidance on credit loss provisions for 2024.

Since the onset of the CRE market deterioration, the Group aims to assess the downside risk of additional credit losses in its higher risk non-recourse portfolio through a temporary bespoke stress testing focused on stressing property values as main driver of loss severity. Stressed values are derived by applying an observed peak-to-trough market index decline (a commercial property value market index to the appraised values) plus an additional haircut, differentiated by property type and region. Implying a liquidation scenario, the stress analysis assumes a loss to occur on a loan when the stressed property value is less than the outstanding loan balance, i.e., the stress LTV beyond 100 %. For the stress analysis performed as of December 31, 2023, the Group applied haircuts ranging from 3 %-18 % on top of the observed peak-to-trough market index decline for each property type. Driven by further deterioration of market conditions especially in the U.S. office sector, the index decline in the fourth quarter increased further compared to the prior quarter.

Based on the stress test assumptions and utilizing the December 31, 2023, stress-tested CRE portfolio of € 31.2 billion as jump-off point, the stress scenario could result in approximately € 1.1 billion of credit losses over multiple years based on the respective maturity profile. The stress loss is reported gross of allowance, however, it should be noted that it covers a portfolio, where the Group has already a credit loss allowance in place. Nevertheless, the stress loss under a liquidation scenario could exceed the current ECL estimate.

The bespoke stress test has numerous limitations, including but not limited to lack of differentiation based on individual asset performance, specific location or asset desirability, all of which could have a material impact on potential stress losses. Furthermore, calculated stress losses are sensitive to potential further deterioration of peak-to-trough index values and assumptions about incremental haircuts, incremental stress loss can therefore change going forward. Changes in underlying assumptions could lead to a wider range of stress results and hence the Group's bespoke stress approach is one of multiple scenarios.

Based on currently available information, Deutsche Bank believes that the ECL estimate related to the Group’s CRE portfolio is within reasonable ranges, requires no additional corrective measure and thus represents the bank’s best estimate. However, the Group remains highly selective around new business, focusing on more resilient property types such as industrial or logistics.

Overall Assessment of ECL’s

To ensure that Deutsche Bank’s ECL model accounted for the uncertainties in the macroeconomic environment throughout 2023, the Group continued to review emerging risks, assessed potential baseline and downside impacts and required actions to manage the bank’s credit strategy and risk appetite. The outcome of these reviews concluded that the bank adequately provisioned for its expected credit losses as of December 31, 2023, and December 31, 2022.

Results from the above reviews and development of key portfolio indicators are regularly discussed at the Credit Risk Appetite and Management Forum and Group Risk Committee. Where necessary, actions and measures are taken to mitigate the risks. Client ratings are regularly reviewed to reflect the latest macroeconomic developments and where potentially significant risks are identified clients are moved to the watchlist (Stage 2), forbearance measures may be negotiated, and credit limits and collateralization are reviewed. Overall, the Group believes that based on its day-to-day risk management activities and regular reviews of emerging risks it has adequately provided for its ECL.

However, the section below further considers whether any additional overlays were required as of year end 2023.

Management overlays applied to the IFRS 9 model output

The Group regularly reviews the IFRS 9 methodology and processes, key inputs into the ECL calculation and discusses upcoming model changes, potential model imprecisions or other estimation uncertainties, for example in the macroeconomic environment to determine if any material overlays are required.

In 2023, the € 92 million overlay related to parameter recalibrations required due to the new definition of default has been released (which at first application led to a decrease of Allowance for Credit Losses). This overlay was introduced in 2021, when 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 related PD and LGD parameters were not updated in the model.

The envisaged PD and LGD recalibrations took place in the second half of 2023 after additional empirical data was available to enable the statistical recalibration and led to the expected parameter updates. The overall net recalibration effect including offsetting impact from the release of the overlay amounted to € 15 million.

For year end 2022, the Group recorded overlays amounting to € (92) million in total (which led to a decrease of Allowance for Credit Losses) versus € 84 million for year end 2023 (which led to an increase of Allowance for Credit Losses), mainly in relation to envisaged ECL model changes planned for implementation in the second quarter of 2024.

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 2023, 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, 2023, the Group did not identify any model weaknesses that would require an additional overlay, except for the ECL model related changes, for which overlays have been recorded.

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, 2023, and December 31, 2022. 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 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 lower as of December 31, 2023, compared to December 31, 2022, due to portfolio changes and minor improvements of base MEV projections which the analyses were based on.

IFRS 9 – Sensitivities of forward-looking information applied on Stage 1 and Stage 2 – Group Level

	December 31, 2023
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impactin € m.	Downward shift	ECL impactin € m.
GDP growth rates	1pp	(80.4)	(1)pp	88.9
Unemployment rates	(0.5)pp	(43.1)	0.5pp	45.9
Real estate prices2	5%	(5.9)	(5)%	6.2
Equities	10%	(9.0)	(10)%	12.2
Credit spreads	(40)%	(20.5)	40%	22.8
Commodities¹	10%	(8.5)	(10)%	9.2

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

² For a more severe stress test relating to the CRE portfolio that also takes into consideration existing and potential exposure in Stage 3 reference is made to the section on Commercial Real Estate above

	December 31, 2022
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impactin € m.	Downward shift	ECL impactin € 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
Commodities	10%	(14.8)	(10)%	15.6

At the divisional level, the sensitivity analysis below was performed for the year ended December 31, 2023, and 2022, 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, 2023
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impactin € m.	Downward shift	ECL impactin € m.
GDP growth rates	1pp	(18.1)	(1)pp	20.7
Unemployment rates	(0.5)pp	(10.4)	0.5pp	11.0
Real estate prices2	5%	(1.5)	(5)%	1.6
Credit spreads	(40)%	(3.8)	40%	4.4
Commodities¹	10%	(2.6)	(10)%	2.9

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

² For a more severe stress test relating to the CRE portfolio that also takes into consideration existing and potential exposure in Stage 3 reference is made to the section on Commercial Real Estate above

	December 31, 2022
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impactin € m.	Downward shift	ECL impactin € 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
Commodities	10%	(4.3)	(10)%	4.6

IFRS 9 – Sensitivities of forward-looking information applied on Stage 1 and Stage 2 - Investment Bank

	December 31, 2023
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impactin € m.	Downward shift	ECL impactin € m.
GDP growth rates	1pp	(30.6)	(1)pp	33.5
Unemployment rates	(0.5)pp	(6.7)	0.5pp	7.5
Real estate prices2	5%	(4.0)	(5)%	4.2
Equities	10%	(3.3)	(10)%	4.4
Credit spreads	(40)%	(13.6)	40%	14.7
Commodities¹	10%	(5.5)	(10)%	5.9

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

² For a more severe stress test relating to the CRE portfolio that also takes into consideration existing and potential exposure in Stage 3 reference is made to the section on Commercial Real Estate above.

	December 31, 2022
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impactin € m.	Downward shift	ECL impactin € 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
Commodities	10%	(9.8)	(10)%	10.3

IFRS 9 – Sensitivities of forward-looking information applied on Stage 1 and Stage 2 - Private Bank

	December 31, 2023
	Upward sensitivity		Downward sensitivity
	Upward shift	ECL impactin € m.	Downward shift	ECL impactin € m.
GDP growth rates	1pp	(24.4)	(1)pp	26.3
Unemployment rates	(0.5)pp	(22.4)	0.5pp	23.3

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

Impact of lifetime expected credit losses for Stage 1 borrowers

As described above, 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 € 5.7 billion as of December 31, 2023 and € 5.6 billion as of December 31, 2022 would increase by approximately 38 % as of year end 2023 and as of year-end 2022, respectively.

Stage 3 LGD setting

The Group’s allowance for credit losses in Stage 3 for the homogeneous portfolios amounts to € 2.2 billion as of December 31, 2023 and € 1.9 billion as of December 31, 2022. 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, 2023 by approximately € 22 million (thereof € 14 million in Germany, € 5 million in Italy and € 2 million in Spain), and by approximately € 19 million as of December 31, 2022 (thereof € 11 million in Germany, € 5 million in Italy and € 2 million in Spain).

IFRS 9 model results

In 2023, provision for credit losses was € 1.5 billion, compared to € 1.2 billion recorded for the year ended 2022. The increase is reflecting an overall weakened macroeconomic environment with only slow recovery of the main economies after Russia’s invasion of Ukraine in 2022 as well as increased challenges for the Commercial Real Estate sector especially in the office space which was already affected by a post-Covid driven change in demand. This sector came under further pressure by monetary measures imposed by the central banks in reply to increased inflation which subsequently led to increased refinancing risks.

The total provisions in 2023 include a € 51 million release related to clients in Russia and Ukraine as a result of successful de-risking activities compared to a € 114 million increase in 2022.

In 2023, € 33 million releases of provision for credit losses related to Stage 1 and 2 and € 1.5 billion new provision for credit losses to Stage 3, this compares to € 204 million provisions of Stage 1 and 2 provisions and € 1.0 billion in Stage 3 in 2022. The reduction of Stage 1 and 2 provisions was primarily driven by the improvement and stabilization of macroeconomic parameters after the macroeconomic outlook significantly deteriorated in 2022 following Russia’s invasion of Ukraine as well as benefits from a one-time model change. The increase of Stage 3 provisions was affecting all businesses but primarily the Private Bank and the Investment Bank.

Corporate Bank recorded provision for credit losses of € 266 million in 2023 versus a € 335 million in 2022. The year on year decrease was primarily driven by Stage 1 and 2 releases in 2023 compared to charges observed in 2022 following an improved macro-economic outlook and benefits from model related changes. This was partly offset by moderately higher Stage 3 provisions compared to the prior year across various portfolios. The Investment Bank recorded an increase of provision for credit losses of € 431 million in 2023 versus € 319 million in 2022. The increase was mainly driven by an increased number of impairments primarily in Commercial Real Estate. The Private Bank recorded an increase of provisions for credit losses of € 783 million in 2023 versus € 583 million reported in 2022. The increase was mainly driven by Stage 3 provisions in the first quarter of 2023 due to single name cases in Wealth Management and in the fourth quarter 2023 due to the reversal of the € 92 million overlay related to parameter recalibrations required due to the new definition of default introduced in 2021.

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, 2022, and as of December 31, 2023.

For details on the Group’s accounting policy related to IFRS 9 Impairment, please refer to Note 1 - Material accounting policies and critical accounting estimates of the consolidated financial statements.

Exposure to Russia

Deutsche Bank continues to have limited exposure 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 € 0.6 billion as of December 31, 2023 (€ 0.8 billion as of December 31, 2022) and other receivables, which are subject to IFRS 9 impairment, and correspondent allowance for credit losses by stages as of December 31, 2023, and December 31, 2022.

Breakdown of total exposure and allowance for credit losses by stages

	Dec 31, 2023			Dec 31, 2022
in € m.	Total Exposure	Allowance for Credit Losses1	Total collateral and guarantees	Total Exposure	Allowance for Credit Losses1	Total collateral and guarantees
Stage 1	93	0	24	209	0	59
Stage 2	797	4	270	1,182	10	375
Stage 3	325	16	261	336	68	152
Total	1,215	20	555	1,726	79	586

¹ Allowance for credit losses do not include allowance for country risk amounting to € 3 million as of December 31, 2023 and € 11 million as of December 31, 2022

Total exposure of € 1.2 billion (€ 1.7 billion as of December 31, 2022) consists of € 0.5 billion (€ 0.8 billion as of December 31, 2022) loan exposure to Russia, € 26 million (€ 78 million as of December 31, 2022) of undrawn commitments and € 0.6 billion (€ 0.8 billion as of December 31, 2022) of unsecured overnight deposits in Rubles with the Central Bank of Russia (which continues to be reflected in Stage 2 as of December 31, 2023); the residual unsecured exposure, excluding the unsecured overnight deposits in Rubles with the Central Bank of Russia, is mainly driven by loans with large Russian companies. Not included in these exposures are Deutsche Bank’s interests in its Russian consolidated entities. This disclosure should be read in conjunction with the section “Risk Factors” in this report.

Managing and Mitigation of credit risk [Abstract]

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

Managing and mitigation of credit risk

Managing credit risk on counterparty level

Credit-relevant 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 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 due to standardized products and processes. 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 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 deteriorated risk/ loss.

In instances where Deutsche Bank has identified counterparties with emerging concern about their credit quality deteriorating or 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 at an early stage to ensure that credit exposures with increased risks are effectively managed, the Bank’s risk management tools are appropriately applied aiming to 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 for individual counterparties are established by the Credit Risk Management function applying credit authorities assigned to individual Credit Officers. This also applies to settlement risk that must fall within limits pre-approved by Credit Risk Management and in a manner that reflects expected settlement patterns for the subject counterparty. Credit approvals are documented by electronic signature under 4-eye principle by the respective credit authority holders and are retained for future reference.

Credit authority is generally assigned as a 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 credit authority is insufficient to establish required credit limits, the transaction is referred to a credit authority holder with the respective credit authority or if exceeding the highest personal authority 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 determine counterparty credit quality by assigning internal ratings and the alignment of the exposure with the Bank’s counterparty concentration risk guidelines, Deutsche Bank also uses various credit risk mitigation & protection techniques to optimize credit exposure and reduce potential credit losses. These techniques 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) team or entity; 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 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 the following two types of credit protection approaches:

– Funded Credit Protection like 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

– Unfunded Credit Protection like Guarantees, which complement 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 (unfunded) risk participations typically fall into this category. Guarantees and strong letters of comfort provided by correlated group members of customers (generally the parent company) may also be accepted and considered in approved rating approaches; 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 or 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, appropriately 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 unfunded credit protection like guarantees, the process for the analysis of the guarantor’s creditworthiness is aligned to the credit assessment process for credit-relevant 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 Management (ERM) Portfolio 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, ERM Portfolio 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 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 risk management 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 risk management 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). 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 by ERM.

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 and factored into financial threshold setting considerations where relevant. Deutsche Bank’s country risk ratings represent a key tool in its management of country risk. They include:

– Sovereign rating (set and managed by ERM Risk Research): A measure of the probability of the sovereign defaulting on its foreign or local currency obligations

– Transfer risk rating (set and managed by ERM Risk research): 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 sets 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.

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. Risk analyses are performed on portfolio level including further breakdown into business units as well as countries/regions. In Wealth Management, target levels are set for global concentrations along products as well as based on type and liquidity of collateral.     

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 asset class, 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 assumes underwriting risk with respect to Commercial Real Estate loans. Primarily in the Commercial Real Estate business unit in the Investment Bank 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.

Deutsche Bank also provides material underwriting activity through its Debt Capital Markets desk which is focused on supporting Investment Grade and cross-over rated corporate borrowers, usually in connection with M&A transaction financing. These exposures are typically 12-24 month bridge loans, which are expected to be repaid by syndicated loans and/or capital markets issuance by the borrower. Deutsche Bank does not bear market placement or pricing risk on these exposures but faces funding risk and credit risk for the duration of the commitment, which are managed through notional underwriting limits for the Group and an industry concentration framework.

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 the bank’s 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 versus 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 historical simulation model (internal model approach) predominantly utilizing full revaluation, although some portfolios remain on a sensitivity-based approach. The approach is used for both Risk Management and capital requirements. 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 included 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 financial credit crisis 2008/09), 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 99 th 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) are 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 optionality.

Liquidity Risk Management [Abstract]

Liquidity Risk Management Framework [text block]

Liquidity risk arises from Deutsche Bank Group’s potential inability to meet payment obligations when they come due or without incurring excessive costs. The Group’s risk taxonomy differentiates between two aspects of liquidity risk: Short-term liquidity risk and Structural funding risk, both embedded in one liquidity risk management framework. Its objective is to ensure that all necessary governance 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 Management Board 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 and funding risk framework The Management Board defines the liquidity and funding risk strategy for the Group and sets the risk appetite, based on recommendations made by the Group Asset and Liability Committee and Group Risk Committee. The Management Board reviews and approves the risk appetite at least annually. The risk appetite is applied to the Group and to bespoke Key Liquidity Entities e.g., Deutsche Bank AG to monitor and control liquidity risk as well as the Group’s long-term funding and issuance plan. The Liquidity Risk Management Framework defines the organization of the liquidity managing functions in alignment with the three lines of defense structure, which is described in the “Risk Management Policy”. The Corporate Divisions and Treasury comprise the first line of defense, responsible for executing the steps needed to most effectively manage the liquidity of the Group and steer business activities. Risk comprises the second line of defense, responsible for defining the liquidity risk management framework, providing independent risk oversight, challenge, and validation of activities conducted by the first line of defense, including establishing the risk appetite. Group Audit comprises the third line of defense, responsible for overseeing the activities of both the first line of defense and second line of defense. The Group Asset and Liability Committee is the Group’s decisive governing body mandated by the Management Board to optimize the sourcing and deployment of the Group’s balance sheet and financial resources in line with the Management Board’s risk appetite and strategy. The Group Asset and Liability Committee has the overarching responsibility to define, approve and optimize the Group’s funding strategy. Regarding the second line of defense the Group Risk Committee is mandated by the Management Board with decision-making authority regarding material risk-related topics. In addition, it reviews and recommends items for Management Board approval, including key risk management principles, the Group’s risk appetite statement, recovery plan, the contingency funding plan, over-arching risk appetite parameters, and recovery and escalation indicators. The Group’s liquidity risk management principles are documented in the “Liquidity Risk Management Policy” and the framework is described in the “Global Liquidity Risk Framework” and “Global Funding Risk Framework” documents. Both the policy and framework documents adhere to and articulate how the eight key risk management practices are applied to liquidity risk, namely risk governance, risk organization (3 lines of defense), risk culture, risk appetite and -strategy, risk identification and -assessment, risk mitigation and controls, risk measurement and reporting, stress planning and -execution. The individual roles and responsibilities are laid out and documented in the Global Responsibility Matrix, which provides further clarity and transparency on the roles and responsibilities across all involved stakeholders. 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 “Liquidity Risk Management Policy” and are subject to the standards it sets forth. In accordance with the European Central Bank’s Supervisory Review and Evaluation Process (and revised Internal Liquidity Adequacy Assessment Process requirement issued in November 2018), the Group has implemented an Internal Liquidity Adequacy Assessment Process, which is assessed, documented and reviewed at least annually and approved by the Management Board. As part of an annual strategic planning process, Treasury projects the development of the key liquidity and funding metrics including the U.S. Dollar currency exposure based on anticipated business activities to ensure that the strategic plan can be executed in accordance with the Group’s risk appetite. Risk appetite and control setting 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 ”Key Limits” based on risk appetite, as well as a suite of additional limits, thresholds and early warning indicators. Deutsche Bank implemented a dedicated Risk Appetite framework covering regulatory Pillar 1as well as internal stress metrics (Pillar 2) which are defined by Liquidity Risk Management and ensure the Group’s liquidity position is balanced across the Group, its Key Liquidity Entities and across currencies. Treasury manages liquidity and funding, in accordance with the risk appetite across a range of relevant metrics and implements several tools including business level risk appetite limits further cascading aspects of risk appetite to divisional level, to ensure compliance. As such, Treasury works closely with Liquidity Risk Management under its delegated authority 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 Deutsche Bank AG hubs and local liquidity shortfalls can be met through support from these hubs. Transfers of liquidity capacity between entities are subject to the approval framework involving the Group’s liquidity steering function as well as the local liquidity managers considering the compliance with Pillar 1 metrics like Liquidity Coverage Ratio (LCR), Net Stable Funding Ratio (NSFR) as well as stressed Net Liquidity Position (sNLP), which is a Pillar 2 metric. 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 Management Board 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 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 and funding diversification below).

The global liquidity stress testing process is managed by Treasury towards a respective 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. Finance -Liquidity & Treasury Reporting & Analysis and Finance - Global Reporting are responsible for implementing these methodologies and performing the stress test calculation in conjunction with Treasury, Liquidity Risk Management, Group Strategic Analytics 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. 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 Deutsche Bank 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 and with that all portfolios and balance sheet, 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 lending 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. 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”.

Funding Risk Management [text block]

Funding Risk Management and Funding Diversification Funding Management 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. The Group relies on a vast range of funding sources such as e.g. deposits, unsecured wholesale funding, Capital Markets Issuances and secured funding. 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 of these. 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 Risk thresholds in order to monitor tenor concentration and counterparty concentration for both secured and unsecured funding sources. In addition, the stability of Deutsche Bank 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. Deutsche Bank is performing ad-hoc analyzes 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 monthly and presented to Group Asset and Liability Committee. 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 modelled 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 modelling assumptions. Short-term balance sheet items (less than one year) 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 i.e. USD. To diversify our refinancing activities Deutsche Bank actively uses among others Capital Markets Issuance.

High Quality Liquid Assets [text block]

High Quality Liquid Assets High-quality Liquid Assets (HQLA) is a Pillar 1 calculation which feeds into LCR and, since the beginning of the fourth quarter 2023 is a key limit per the risk appetite, replacing Liquidity Reserve. HQLA comprise available cash and cash equivalents and unencumbered high quality liquid securities (including government and government guaranteed bonds), representing the most readily available and most important countermeasure in a stress event.

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 and liquidity risk management). This is supported by risk appetite including 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 is 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. Data, methodologies and industry standards for measuring and assessing climate and other environmental risks are still evolving or, in certain cases, are not yet available. This, combined with a lack of comprehensive and consistent climate and other environmental risk disclosures by its clients means that the bank, in line with the wider industry, remains partially reliant on proxy estimates and qualitative approaches when assessing these risks which introduces a higher degree of uncertainty into climate-related disclosures.

Financial institutions are facing increased scrutiny as well as reporting requirements on climate and broader ESG-related issues from governments, regulators, shareholders and other bodies. This can lead to reputational risks if the bank is not seen to support the transition to a lower carbon economy or is perceived to be heavily involved in the financing of activities harmful to nature or linked to human rights abuse within supply chains.

Certain jurisdictions have begun to develop anti-ESG measures including requiring financial institutions that wish to do business with them to certify their non-adherence to aspects of the transition agenda. Failing to comply with these requirements may result in the termination of existing business and the inability to conduct new business with those jurisdictions, while complying may lead to reputational risks.

Deutsche Bank is committed to managing its business activities and operations in a sustainable manner, including aligning its portfolios with net zero emissions by 2050. In October, Deutsche Bank published its Initial Transition Plan (*), which documents the strategy that the bank has developed to decarbonize its operations, its upstream supply chain and its financing portfolio. In the plan the Group also disclosed net-zero aligned decarbonization pathways for three additional carbon intensive sectors compared to 2022, bringing the total number of sectors covered by portfolio targets to seven: Oil and Gas (upstream), Power Generation, Automotive (light duty vehicles), Steel, Coal mining, Cement and Shipping. Targets and metrics disclosed in the bank’s Transition Plan are integrated into the Group-wide risk management framework, appetite and controls.

The Group Sustainability Committee acts as the main governance and decision-making body for sustainability related matters across Deutsche Bank. This includes the assessment of material impacts as well as risks and opportunities for Deutsche Bank. The Management Board has delegated sustainability related decisions to this committee, which is chaired by the Chief Executive Officer and the Chief Sustainability Officer (Vice Chair).

The Group Risk Committee, chaired by the Chief Risk Officer and established by the Management Board has the mandate to oversee several risk & capital related matters. This includes the responsibility for developing the bank’s Climate Risk Framework. The Committee approves the Bank’s climate and environmental risk appetite, including appetite for deviation from net-zero decarbonization linear reduction pathways. A number of other committees of the Group Risk Committee are responsible for the development and management of specific elements of climate and environmental risk.

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.