The graphs comparing the respective Central scenarios in the fourth quarters of 2019 and 2020 reveal the extent of economic dislocation that occurred in 2020 and the impact this has had on central projections made at the end of 2019.

The emergent nature of the Covid-19 outbreak at the end of 2019 meant that, consistent with other banks, HSBC’s Central scenario did not, on a forward-looking basis, consider the impact of the virus. Our Central scenario at the 2019 year-end projected moderate growth over a five-year horizon, with strong prospects for employment and a gradual increase in policy interest rates by central banks in the major economies of Europe and North America. The onset of the virus led to a fundamental reassessment of our Central forecast and the distribution of risks over the course of 2020. Our Central scenario at the end of 2020, as described above, is based on assumptions that are considerably different.

GDP growth: Comparison


UK

Note: Real GDP shown as year-on-year percentage change.


Hong Kong

Note: Real GDP shown as year-on-year percentage change.


US

Note: Real GDP shown as year-on-year percentage change.


Mainland China

Note: Real GDP shown as year-on-year percentage change.

The consensus Upside scenario

Compared with the consensus Central scenario, the consensus Upside scenario features a faster recovery in economic activity during the first two years, before converging to long-run trends.

The scenario is consistent with a number of key upside risk themes. These include the orderly and rapid global abatement of Covid-19 via successful containment and prompt deployment of a vaccine; de-escalation of tensions between the US and China; de-escalation of political tensions in Hong Kong; continued support from fiscal and monetary policy and smooth relations between the UK and the EU, which enables the two parties to swiftly reach a comprehensive agreement on trade and services.

The following table describes key macroeconomic variables and the probabilities assigned in the consensus Upside scenario.

Consensus Upside scenario best outcome

Hong
Kong

Mainland
China

Canada

France

UAE

Mexico

GDP growth rate

19.9 (2Q21)

11.8 (2Q21)

13.8 (4Q21)

20.5 (1Q21)

15.8 (2Q21)

19.5 (2Q21)

13.8 (4Q21)

16.8 (2Q21)

Unemployment rate

3.7 (4Q22)

3.9 (4Q22)

3.0 (3Q22)

3.9 (4Q21)

5.3 (3Q22)

7.9 (4Q22)

2.2 (4Q21)

3.6 (3Q22)

House price growth

6.9 (4Q22)

6.4 (1Q22)

4.9 (1Q22)

12.2 (1Q22)

5.2 (1Q21)

5.7 (2Q22)

18.5 (1Q22)

8.2 (3Q22)

Short-term interest rate

0.1 (2Q22)

0.4 (1Q21)

1.1 (1Q21)

3.0 (1Q21)

0.6 (1Q21)

(0.4) (1Q21)

0.9 (1Q21)

5.0 (1Q21)

Probability consensus Upside

Note: Extreme point in the consensus Upside is ‘best outcome’ in the scenario, for example the highest GDP growth and the lowest unemployment rate, in the first two years of the scenario.

Downside scenarios

The year 2021 is expected to be a period of economic recovery, but the progression and management of the pandemic presents a key risk to global growth. A new and more contagious strain of the virus increased the transmission rate in the UK and resulted in stringent restrictions to mobility towards the end of 2020. This viral strain observed in the UK, together with aggressive strains observed in other countries including South Africa and Brazil, introduce the risk that transmission may increase significantly within the national borders of a number of countries in 2021 and also raise concerns around the efficacy of vaccines as the virus mutates. Some countries may keep significant restrictions to mobility in place for an extended period of time and at least until critical segments of the population can be inoculated. Further risks to international travel also arise.

A number of vaccines have been developed and approved for use at a rapid pace and plans to inoculate significant proportions of national populations in 2021 across many of our key markets are a clear positive for economic recovery. While we expect vaccination programmes to be successful, governments and healthcare authorities face country-specific challenges that could affect the speed and spread of vaccinations. These challenges include the logistics of inoculating a significant proportion of national populations within a limited timeframe and the public acceptance of vaccines. On a global level, supply challenges could affect the pace of roll-out and the efficacy of vaccines is yet to be determined.

Government support programmes in advanced economies in 2020 were supported by accommodative actions taken by central banks. These measures by governments and central banks have provided households and firms with significant support. An inability or unwillingness to continue with such support or the untimely withdrawal of support present a downside risk to growth.

While Covid-19 and related risks dominate the economic outlook, geopolitical risks also present a threat. These risks include:

•Continued long-term differences between the US and China, which could affect sentiment and restrict global economic activity.

•The Covid-19 outbreak reduced the incidence of protests in Hong Kong. Despite the passage of the national security law in 2020, such unrest has the potential to return as the virus abates and restrictions to mobility ease.

•The Trade and Cooperation Agreement between the UK and EU averted a disorderly UK departure from the EU, but the risk of future disagreements remains, which may hinder the ability to reach a more comprehensive agreement on trade and services.

The consensus Downside scenario

In the consensus Downside scenario, economic recovery is considerably weaker compared with the Central scenario. GDP growth remains weak, unemployment rates stay elevated and asset and commodity prices fall before gradually recovering towards their long-run trends.

The scenario is consistent with the key downside risks articulated above. Further outbreaks of Covid-19, coupled with delays in vaccination programmes, lead to longer-lasting restrictions on economic activity in this scenario. Other global risks also increase and drive increased risk-aversion in asset markets.

The following table describes key macroeconomic variables and the probabilities assigned in the consensus Downside scenario.

Consensus Downside scenario worst outcome

Hong
Kong

Mainland
China

Canada

France

UAE

Mexico

GDP growth rate

(7.6) (1Q21)

(3.4) (1Q21)

(2.1) (3Q21)

(1.3) (4Q21)

(3.6) (1Q21)

(3.0) (1Q21)

(7.3) (1Q21)

(8.0) (1Q21)

Unemployment rate

9.4 (4Q21)

8.2 (2Q21)

6.4 (1Q21)

4.3 (3Q22)

9.2 (1Q21)

11.2 (1Q21)

3.0 (1Q21)

6.2 (3Q21)

House price growth

(10.8) (4Q21)

0.1 (3Q21)

(6.8) (3Q21)

0.3 (4Q21)

(1.3) (1Q22)

(3.3) (2Q21)

(19.2) (2Q21)

1.0 (4Q21)

Short-term interest rate

0.1 (1Q21)

0.3 (1Q22)

1.1 (4Q22)

2.8 (1Q21)

0.5 (1Q21)

(0.5) (1Q21)

0.8 (1Q22)

3.8 (1Q21)

Probability consensus Downside

Note: Extreme point in the consensus Downside is 'worst outcome' in the scenario, for example lowest GDP growth and the highest unemployment rate, in the first two years of the scenario.

Additional Downside scenario

An additional Downside scenario that features a global recession has been created to reflect management’s view of severe risks. In this scenario, infections rise in 2021 and setbacks to vaccine programmes imply that successful roll-out of vaccines only occurs towards the end of 2021 and it takes until the end of 2022 for the

pandemic to come to an end. The scenario also assumes governments and central banks are unable to significantly increase fiscal and monetary programmes, which results in abrupt corrections in labour and asset markets.

The following table describes key macroeconomic variables and the probabilities assigned in the additional Downside scenario.

Additional Downside scenario worst outcome

Hong
Kong

Mainland
China

Canada

France

UAE

Mexico

GDP growth rate

(10.1) (1Q21)

(4.2) (1Q21)

(8.3) (4Q21)

(9.5) (4Q21)

(5.0) (1Q21)

(6.7) (1Q21)

(12.2) (1Q21)

(10.9) (1Q21)

Unemployment rate

9.8 (3Q21)

11.4 (4Q22)

6.7 (3Q21)

6.1 (3Q22)

11.3 (1Q21)

12.3 (1Q21)

3.9 (1Q21)

6.9 (4Q21)

House price growth

(14.5) (4Q21)

(9.3) (3Q21)

(21.0) (4Q21)

(19.4) (4Q21)

(10.4) (4Q21)

(7.1) (3Q21)

(22.9) (2Q21)

(2.7) (4Q21)

Short-term interest rate

0.8 (2Q21)

1.1 (1Q21)

1.3 (1Q21)

4.0 (2Q21)

0.4 (1Q21)

0.2 (2Q21)

0.5 (3Q21)

6.7 (2Q21)

Probability additional Downside

Note: Extreme point in the additional Downside is 'worst outcome' in the scenario, for example lowest GDP growth and the highest unemployment rate, in the first two years of the scenario.

In considering economic uncertainty and assigning probabilities to scenarios, management has considered both global and country-specific factors. This has led management to assigning scenario probabilities that are tailored to its view of uncertainty in individual markets.

To inform its view, management has considered trends in the progression of the virus in individual countries, the expected reach and efficacy of vaccine roll-outs over the course of 2021, the size and effectiveness of future government support schemes and the connectivity with other countries. Management has also been guided by the actual response to the Covid-19 outbreak and by the economic experience across countries in 2020. China’s visible success at containing the virus and its repeated rapid response to localised outbreaks, coupled with government support programmes and clear signs of economic recovery, have led management to conclude that the economic outlook for mainland China is the least volatile out of all our top markets. The Central scenario for mainland China has an 80% probability while a total of 10% has been assigned to the two Downside scenarios. In Hong Kong, the combination of recurrent outbreaks, a lack of details around the roll-out of a vaccination programme and the other risks outlined above, have led management to assign 25% weight to the two Downside scenarios.

The UK and France face the greatest economic uncertainty in our key markets. In the UK, the discovery of a more infectious strain of the virus and subsequent national restrictions on activity imposed before the end of 2020 have resulted in considerable uncertainty in the economic outlook. In France, the increases in cases and hospitalisations towards the end of 2020, the difficulties experienced with the launch of a national vaccination programme and the wide range of measures taken to restrict activity similarly affect the economic outlook. Given these considerations, the Central and the consensus Downside scenario for the UK and France have each been assigned 40% probability. This reflects management’s view that, as a result of elevated uncertainty in these two markets, the Central scenario cannot be viewed as the single most likely outcome. The additional Downside scenario has been assigned 15% probability to reflect the view that the balance of risks is weighted to the downside.

Uncertainty related to the continued impact of the pandemic and the ability of governments to control its spread via restrictions and vaccinations over the course of 2021 also play a prominent role in assigning scenario weights to our other markets. In addition, for the US, Canada and Mexico, connectivity across the three North American economies has been considered. In the UAE, the impact of the oil price on the economy and the ability of non-oil sectors to contribute to economic recovery have influenced the view of uncertainty. The Central scenario has been assigned between 65% and 70% weight for these four markets and, with risks perceived as being weighted to the downside, the two Downside scenarios have been given weights between 20% and 30%.

The following graphs show the historical and forecasted GDP growth rate for the various economic scenarios in our four largest markets.


US


UK


Hong Kong


Mainland China

Critical accounting estimates and judgements

The calculation of ECL under IFRS 9 involves significant judgements, assumptions and estimates. The level of estimation uncertainty and judgement has increased during 2020 as a result of the economic effects of the Covid-19 outbreak, including significant judgements relating to:

•the selection and weighting of economic scenarios, given rapidly changing economic conditions in an unprecedented manner, uncertainty as to the effect of government and central bank support measures designed to alleviate adverse economic impacts, and a wider distribution of economic forecasts than before the pandemic. The key judgements are the length of time over which the economic effects of the pandemic will occur, the speed and shape of recovery. The main factors include the effectiveness of pandemic containment measures, the pace of roll-out and effectiveness of vaccines, and the emergence of new variants of the virus, plus a range of geopolitical uncertainties, which together represent a very high degree of estimation uncertainty, particularly in assessing Downside scenarios;

•estimating the economic effects of those scenarios on ECL, where there is no observable historical trend that can be reflected in the models that will accurately represent the effects of the economic changes of the severity and speed brought about by the Covid-19 outbreak. Modelled assumptions and

linkages between economic factors and credit losses may underestimate or overestimate ECL in these conditions, and there is significant uncertainty in the estimation of parameters such as collateral values and loss severity; and

•the identification of customers experiencing significant increases in credit risk and credit impairment, particularly where those customers have accepted payment deferrals and other reliefs designed to address short-term liquidity issues given muted default experience to date. The use of segmentation techniques for indicators of significant increases in credit risk involves significant estimation uncertainty.

How economic scenarios are reflected in ECL calculations

Models are used to reflect economic scenarios on ECL estimates. As described above, modelled assumptions and linkages based on historical information could not alone produce relevant information under the unprecedented conditions experienced in 2020, and it was necessary to place greater emphasis on judgemental adjustments to modelled outcomes than in previous years.

We have developed globally consistent methodologies for the application of forward economic guidance into the calculation of ECL for wholesale and retail credit risk. These standard approaches are described below, followed by the management judgemental adjustments made, including those to reflect the circumstances experienced in 2020.

For wholesale, a global methodology is used for the estimation of the term structure of probability of default (‘PD’) and loss given default (‘LGD’). For PDs, we consider the correlation of forward economic guidance to default rates for a particular industry in a country. For LGD calculations, we consider the correlation of forward economic guidance to collateral values and realisation rates for a particular country and industry. PDs and LGDs are estimated for the entire term structure of each instrument.

For impaired loans, LGD estimates take into account independent recovery valuations provided by external consultants where available or internal forecasts corresponding to anticipated economic conditions and individual company conditions. In estimating the ECL on impaired loans that are individually considered not to be significant, we incorporate forward economic guidance proportionate to the probability-weighted outcome and the Central scenario outcome for non-stage 3 populations.

For retail, the impact of economic scenarios on PD is modelled at a portfolio level. Historical relationships between observed default rates and macroeconomic variables are integrated into IFRS 9 ECL estimates by using economic response models. The impact of these scenarios on PD is modelled over a period equal to the remaining maturity of the underlying asset or assets. The impact on LGD is modelled for mortgage portfolios by forecasting future loan-to-value (‘LTV’) profiles for the remaining maturity of the asset by using national level forecasts of the house price index and applying the corresponding LGD expectation.

These models are based largely on historical observations and correlations with default rates. Management judgemental adjustments are described below.

Management judgemental adjustments

In the context of IFRS 9, management judgemental adjustments are short-term increases or decreases to the ECL at either a customer or portfolio level to account for late-breaking events, model and data limitations and deficiencies, and expert credit judgement applied following management review and challenge. In the Annual Report and Accounts 2019, these were ‘Post-model adjustments’.

The most severe projections at 31 December 2020 of macroeconomic variables are outside the historical observations on which IFRS 9 models have been built and calibrated to operate. Moreover, the complexities of country-specific governmental support programmes, the impacts on customer behaviours and the unpredictable pathways of the pandemic have never been modelled. Consequently, HSBC’s IFRS 9 models, in some cases, generate outputs that appear overly sensitive when compared

with other economic and credit metrics. Governmental support programmes and customer payment reliefs have dislocated the correlation between economic conditions and defaults on which models are based. Management judgemental adjustments are required to help ensure that an appropriate amount of ECL impairment is recognised.

We have internal governance in place to regularly monitor management judgemental adjustments and, where possible, to reduce the reliance on these through model recalibration or redevelopment, as appropriate. During 2020 the composition of modelled ECL and management judgemental adjustments changed significantly, reflecting the path of the pandemic, containment efforts and government support measures, and this is expected to continue to be the case until economic conditions improve. Wider-ranging model changes will take time to develop and need observable loss data on which models can be developed. Models will be revisited over time once the longer-term impacts of Covid-19 are observed. Therefore, we anticipate significant management judgemental adjustments for the foreseeable future.

Management judgemental adjustments made in estimating the reported ECL at 31 December 2020 are set out in the following table. The table includes adjustments in relation to data and model limitations resulting from the pandemic, and as a result of the regular process of model development and implementation. It shows the adjustments applicable to the scenario-weighted ECL numbers. Adjustments in relation to Downside scenarios are more significant, as results are subject to greater uncertainty.


Management judgemental adjustments to ECL1
	Retail	Wholesale	Total
	$bn	$bn	$bn

Low-risk counterparties (banks, sovereigns and government entities)	—	(0.7)	(0.7)
Corporate lending adjustments	—	0.5	0.5
Retail lending PD adjustments	(0.8)		(0.8)
Retail model default suppression adjustment	1.9	—	1.9
Other retail lending adjustments	0.4	—	0.4
Total	1.5	(0.2)	1.3

1 Management judgemental adjustments presented in the table reflect increases or (decreases) to ECL, respectively.

Management judgemental adjustments at 31 December 2019 were an increase to ECL of $75m for the wholesale portfolio and $131m for the retail portfolio. This excludes adjustments for alternative scenarios.

During 2020, management judgemental adjustments reflected the volatile economic conditions associated with the Covid-19 pandemic. The composition of modelled ECL and management judgemental adjustments changed significantly over 2020 as certain economic measures, such as GDP growth rate, passed the expected low point in a number of key markets and returned towards those reflected in modelled relationships, subject to continued uncertainty in the recovery paths of different economies.

At 31 December 2020, wholesale management judgemental adjustments were an ECL reduction of $0.2bn (31 December 2019: $0.1bn increase). These wholesale adjustments were lower than those made in the second and third quarters of 2020 following an improvement in macroeconomic assumptions, with models operating closer to their calibration range and following recalibration for stressed conditions.

The adjustments relating to low-credit-risk exposures are mainly to highly rated banks, sovereigns and US government-sponsored entities, where modelled credit factors did not fully reflect the underlying fundamentals of these entities or the effect of government support and economic programmes in the Covid-19 environment.

Adjustments to corporate exposures principally reflect the outcome of management judgements for high-risk and vulnerable sectors in some of our key markets, supported by credit experts’ input, quantitative analyses and benchmarks. Considerations

include potential default suppression in some sectors due to government intervention and late-breaking idiosyncratic developments.

In the fourth quarter of 2020, retail management judgemental adjustments led to an ECL increase of $1.5bn, primarily from additional ECL of $1.9bn to reflect adjustments to the timing of default, which has been delayed by government support and customer relief measures. This was partly offset by adjustments to retail lending PD outputs, to reduce ECL of $0.8bn for unintuitive model responses, primarily where economic forecasts were beyond the bounds of the model development period. Other retail lending adjustments of $0.4bn led to an increase in ECL from areas such as customer relief and data limitations.

The retail model default suppression adjustment was applied as defaults remain temporarily suppressed due to government support and customer relief programmes, which have supported stabilised portfolio performance. Retail models are reliant on the assumption that as macroeconomic conditions deteriorate, defaults will crystallise. This adjustment aligns the increase in default due to changes in economic conditions to the period of time when defaults are expected to be observed. The retail model default suppression adjustment will be monitored and updated prospectively to ensure appropriate alignment with expected performance taking into consideration the levels and timing of government support and customer relief programmes.

Retail lending PD adjustments are primarily related to an adjustment made in relation to the UK. The downside unemployment forecasts were outside the historical bounds on which the model was developed resulting in unintuitive levels of PD. This adjustment reduced the sensitivity of PD to better align with the historical correlation between changes in levels of unemployment and defaults.

Economic scenarios sensitivity analysis of ECL estimates

Management considered the sensitivity of the ECL outcome against the economic forecasts as part of the ECL governance process by recalculating the ECL under each scenario described above for selected portfolios, applying a 100% weighting to each scenario in turn. The weighting is reflected in both the determination of a significant increase in credit risk and the measurement of the resulting ECL.

The ECL calculated for the Upside and Downside scenarios should not be taken to represent the upper and lower limits of possible ECL outcomes. The impact of defaults that might occur in the future under different economic scenarios is captured by recalculating ECL for loans in stages 1 and 2 at the balance sheet date. The population of stage 3 loans (in default) at the balance sheet date is unchanged in these sensitivity calculations. Stage 3 ECL would only be sensitive to changes in forecasts of future economic conditions if the LGD of a particular portfolio was sensitive to these changes.

There is a particularly high degree of estimation uncertainty in numbers representing tail risk scenarios when assigned a 100% weighting.

For wholesale credit risk exposures, the sensitivity analysis excludes ECL and financial instruments related to defaulted obligors because the measurement of ECL is relatively more sensitive to credit factors specific to the obligor than future economic scenarios. Therefore, it is impracticable to separate the effect of macroeconomic factors in individual assessments. For retail credit risk exposures, the sensitivity analysis includes ECL for loans and advances to customers related to defaulted obligors. This is because the retail ECL for secured mortgage portfolios including loans in all stages is sensitive to macroeconomic variables.

Wholesale and retail sensitivity

The wholesale and retail sensitivity analysis is stated inclusive of management judgemental adjustments, as appropriate to each scenario. The results tables exclude portfolios held by the insurance business and small portfolios, and as such cannot be

directly compared to personal and wholesale lending presented in other credit risk tables. Additionally in both the wholesale and retail analysis, the comparative period results for additional/ alternative Downside scenarios are also not directly comparable with the current period, because they reflect different risk profiles relative to the consensus scenarios for the period end.

Wholesale analysis


IFRS 9 ECL sensitivity to future economic conditions
	Gross carrying amount2	Reported ECL	Central scenario ECL	Upside scenario ECL	Downside scenario ECL	Additional Downside scenario ECL
ECL of loans and advances to customers at 31 December 20201	$m	$m	$m	$m	$m	$m

UK	430,555	2,077	1,514	1,026	2,271	3,869
US	201,263	369	314	219	472	723
Hong Kong	452,983	474	388	211	672	1,363
Mainland China	118,163	116	93	28	252	1,158
Canada	85,720	183	140	82	253	528
Mexico	25,920	246	222	177	285	437
UAE	44,777	250	241	190	330	536
France	164,899	117	109	97	131	238


IFRS 9 ECL sensitivity to future economic conditions3
	Gross carrying amount2	Reported ECL	Central scenario ECL	Upside scenario ECL	Downside scenario ECL	Alternative scenarios ECL4
ECL of loans and advances to customers at 31 December 20191	$m	$m	$m	$m	$m	$m

UK	346,035	725	536	480	635	1,050–2,100
US	203,610	148	149	132	161
Hong Kong	418,102	328	243	241	244	550-700
Mainland China	104,004	124	118	95	106	150
Canada	74,620	80	79	63	108
Mexico	32,632	69	68	48	99
UAE	42,304	97	97	89	108
France	124,618	55	53	50	79

1 ECL sensitivity includes off-balance sheet financial instruments that are subject to significant measurement uncertainty.

2 Includes low credit-risk financial instruments such as debt instruments at FVOCI, which have high carrying amounts but low ECL under all the above scenarios.

3 ECL sensitivities for 2019 exclude portfolios utilising less complex modelling approaches and management judgemental adjustments only included in reported ECL.

4 The UK alternative Downside (‘AD’) scenario 1 had an ECL impact of $1bn with AD2 and AD3 scenarios with ECL impacts of $1.9bn and $2.1bn respectively. The Hong Kong AD1 and AD2 scenarios had an impact of $0.55bn and $0.7bn respectively.

At 31 December 2020, the most significant level of ECL sensitivity was observed in the UK, Hong Kong and mainland China. This higher sensitivity is largely driven by significant exposure in these regions and more severe impacts of the Downside scenarios relative to the Central and probability-weighted scenarios. For mainland China, the additional Downside scenario weighting of

2% reflects a scenario that is considered highly unlikely and is significantly more adverse compared with the Central scenario, resulting in a higher ECL estimate relative to the reported and Central scenarios.


IFRS 9 ECL sensitivity to future economic conditions1
	Gross carrying amount	Reported ECL	Central scenario ECL	Upside scenario ECL	Downside scenario ECL	Additional Downside scenario
ECL of loans and advances to customers at 31 December 20202	$m	$m	$m	$m	$m	$m
UK
Mortgages	146,478	197	182	172	205	221
Credit cards	7,869	857	774	589	904	1,084
Other	9,164	897	795	471	1,022	1,165
Mexico
Mortgages	3,896	111	101	79	136	167
Credit cards	1,113	260	255	243	269	290
Other	2,549	436	428	411	451	491
Hong Kong
Mortgages	89,943	—	—	—	—	—
Credit cards	7,422	266	259	247	277	405
Other	6,020	112	105	102	115	130
UAE
Mortgages	1,889	66	63	53	73	78
Credit cards	426	92	81	62	107	126
Other	683	38	37	33	41	46
France
Mortgages	24,565	68	68	68	69	70
Other	1,725	88	87	85	88	91
US
Mortgages	15,399	41	39	38	41	53
Credit cards	570	86	84	81	88	119
Canada
Mortgages	22,454	31	30	29	31	36
Credit cards	260	9	9	8	9	9
Other	1,775	22	21	20	24	28


IFRS 9 ECL sensitivity to future economic conditions1 (continued)
	Gross carrying amount	Reported ECL	Central scenario ECL	Upside scenario ECL	Downside scenario ECL	Alternative scenarios ECL
ECL of loans and advances to customers at 31 December 20192	$m	$m	$m	$m	$m	$m
UK
Mortgages	130,079	123	33	28	38	50-80
Credit cards	9,359	431	421	376	506	670-930
Other	10,137	382	318	282	374	490-700
Mexico
Mortgages	3,385	32	31	24	41
Credit cards	1,295	211	211	190	231
Other	3,001	341	340	312	380
Hong Kong
Mortgages	86,448	0	0	0	0	0
Credit cards	7,795	243	201	191	201	400
Other	7,446	105	95	90	104	130
UAE
Mortgages	1,983	92	92	83	91
Credit cards	513	54	54	49	72
Other	895	28	28	26	31
France
Mortgages	21,374	60	60	59	60
Other	1,643	73	73	73	74
US
Mortgages	14,732	22	22	21	24
Credit cards	738	68	68	62	74
Canada
Mortgages	19,843	15	14	13	16
Credit cards	270	7	7	7	7
Other	2,231	17	17	16	18

1 ECL sensitivities exclude portfolios utilising less complex modelling approaches.

2 ECL sensitivity includes only on-balance sheet financial instruments to which IFRS 9 impairment requirements are applied.

At 31 December 2020, the most significant level of ECL sensitivity was observed in the UK, Mexico and Hong Kong.

Mortgages reflected the lowest level of ECL sensitivity across most markets as collateral values remain resilient. Hong Kong mortgages had low levels of reported ECL due to the credit quality of the portfolio, and so presented sensitivity was negligible. Credit cards and other unsecured lending are more sensitive to economic forecasts, which have deteriorated in 2020 due to the Covid-19 pandemic.

Group ECL sensitivity results

The ECL impact of the scenarios and management judgemental adjustments are highly sensitive to movements in economic forecasts, including the efficacy of government support measures. Based upon the sensitivity tables presented above, if the Group ECL balance (excluding wholesale stage 3, which is assessed individually) was estimated solely on the basis of the Central scenario, Downside scenario or the additional Downside scenario at 31 December 2020, it would increase/(decrease) as presented in the below table.


	Retail1	Wholesale1
Total Group ECL 2020	$bn	$bn
Reported ECL	4.5	4.5
Scenarios
100% consensus Central scenario	(0.3)	(0.9)
100% consensus Downside scenario	0.3	1.0
100% additional Downside scenario	1.3	5.9


	Retail1	Wholesale
Total Group ECL 2019	$bn	$bn
Reported ECL	2.9	2.0
Scenarios
100% consensus Central scenario	(0.2)	(0.3)
100% consensus Downside scenario	0.1	—
100% alternative Downside scenario	n/a	n/a

1 On the same basis as retail and wholesale sensitivity analysis.

There still remains a significant degree of uncertainty in relation to the UK economic outlook. If a 100% weight were applied to the consensus Downside and additional Downside scenario for the UK, respectively, it would result in an increase in ECL of $0.2bn and $1.8bn in wholesale and $0.2bn and $0.5bn in retail.


Reconciliation of changes in gross carrying/nominal amount and allowances for loans and advances to banks and customers including loan commitments and financial guarantees
(Audited)
	Non-credit impaired				Credit impaired
	Stage 1		Stage 2		Stage 3		POCI		Total
	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL
	$m	$m	$m	$m	$m	$m	$m	$m	$m	$m
At 1 Jan 2020	1,561,613	(1,464)	105,551	(2,441)	14,335	(5,121)	345	(99)	1,681,844	(9,125)
Transfers of financial instruments:	(129,236)	(1,122)	116,783	1,951	12,453	(829)	—	—	—	—
– transfers from stage 1 to stage 2	(298,725)	947	298,725	(947)	—	—	—	—	—	—
– transfers from stage 2 to stage 1	172,894	(2,073)	(172,894)	2,073	—	—	—	—	—	—
– transfers to stage 3	(3,942)	30	(10,320)	986	14,262	(1,016)	—	—	—	—
– transfers from stage 3	537	(26)	1,272	(161)	(1,809)	187	—	—	—	—
Net remeasurement of ECL arising from transfer of stage	—	907	—	(1,158)	—	(750)	—	—	—	(1,001)

New financial assets originated or purchased	437,836	(653)	—	—	—	—	25	(1)	437,861	(654)
Assets derecognised (including final repayments)	(313,347)	160	(37,409)	464	(3,430)	485	(23)	2	(354,209)	1,111
Changes to risk parameters – further lending/repayment	(83,147)	157	29,092	85	(597)	248	(50)	(2)	(54,702)	488
Changes to risk parameters – credit quality	—	(408)	—	(4,374)	—	(4,378)	—	(39)	—	(9,199)
Changes to models used for ECL calculation	—	134	—	294	—	5	—	—	—	433
Assets written off	—	—	—	—	(2,946)	2,944	(30)	30	(2,976)	2,974
Credit-related modifications that resulted in derecognition	—	—	—	—	(23)	7	—	—	(23)	7
Foreign exchange	32,808	(47)	9,123	(223)	633	(163)	4	(3)	42,568	(436)
Others	(76)	5	292	(1)	(1)	8	8	(1)	223	11
At 31 Dec 2020	1,506,451	(2,331)	223,432	(5,403)	20,424	(7,544)	279	(113)	1,750,586	(15,391)
ECL income statement change for the period		297		(4,689)		(4,390)		(40)		(8,822)
Recoveries										326
Others										(84)
Total ECL income statement change for the period										(8,580)


	At 31 Dec 2020		12 months ended 31 Dec 2020
	Gross carrying/nominal amount	Allowance for ECL	ECL charge
	$m	$m	$m
As above	1,750,586	(15,391)	(8,580)
Other financial assets measured at amortised cost	772,408	(175)	(95)
Non-trading reverse purchase agreement commitments	61,716	—	—
Performance and other guarantees not considered for IFRS 9	—	—	(94)
Summary of financial instruments to which the impairment requirements in IFRS 9 are applied/Summary consolidated income statement	2,584,710	(15,566)	(8,769)
Debt instruments measured at FVOCI	399,717	(141)	(48)
Total allowance for ECL/total income statement ECL change for the period	n/a	(15,707)	(8,817)


Reconciliation of changes in gross carrying/nominal amount and allowances for loans and advances to banks and customers including loan commitments and financial guarantees
(Audited)
	Non-credit impaired				Credit impaired				Total
	Stage 1		Stage 2		Stage 3		POCI
	Gross exposure	Allowance/ provision for ECL	Gross exposure	Allowance/ provision for ECL	Gross exposure	Allowance/ provision for ECL	Gross exposure	Allowance/ provision for ECL	Gross exposure	Allowance/ provision for ECL
	$m	$m	$m	$m	$m	$m	$m	$m	$m	$m
At 1 Jan 2019	1,502,976	(1,449)	95,104	(2,278)	14,232	(5,135)	334	(194)	1,612,646	(9,056)
Transfers of financial instruments:	(36,244)	(543)	31,063	1,134	5,181	(591)	—	—	—	—
– transfers from stage 1 to stage 2	(108,434)	487	108,434	(487)	—	—	—	—	—	—
– transfers from stage 2 to stage 1	73,086	(1,044)	(73,086)	1,044	—	—	—	—	—	—
– transfers to stage 3	(1,284)	59	(5,022)	665	6,306	(724)	—	—	—	—
– transfers from stage 3	388	(45)	737	(88)	(1,125)	133	—	—	—	—
Net remeasurement of ECL arising from transfer of stage	—	669	—	(676)	—	(114)	—	—	—	(121)
New financial assets originated or purchased	504,064	(534)	—	—	—	—	135	(21)	504,199	(555)
Assets derecognised (including final repayments)	(352,961)	112	(19,909)	553	(2,712)	656	(26)	8	(375,608)	1,329
Changes to risk parameters – further lending/repayment	(72,239)	291	(2,560)	67	402	(6)	28	12	(74,369)	364
Changes to risk parameters – credit quality	—	2	—	(1,208)	—	(2,704)	—	(51)	—	(3,961)
Changes to models used for ECL calculation	—	(6)	—	4	—	14	—	—	—	12
Assets written off	—	—	—	—	(2,657)	2,657	(140)	140	(2,797)	2,797
Credit-related modifications that resulted in derecognition	—	—	—	—	(268)	125	—	—	(268)	125
Foreign exchange	16,838	(9)	1,201	(40)	160	(31)	1	1	18,200	(79)
Others	(821)	3	652	3	(3)	8	13	6	(159)	20
At 31 Dec 2019	1,561,613	(1,464)	105,551	(2,441)	14,335	(5,121)	345	(99)	1,681,844	(9,125)
ECL income statement change for the period		534		(1,260)		(2,154)		(52)		(2,932)
Recoveries										361
Others										(20)
Total ECL income statement change for the period										(2,591)


	At 31 Dec 2019		12 months ended 31 Dec 2019
	Gross carrying/nominal amount	Allowance for ECL	ECL charge
	$m	$m	$m
As above	1,681,844	(9,125)	(2,591)
Other financial assets measured at amortised cost	615,179	(118)	(26)
Non-trading reverse purchase agreement commitments	53,093	—	—
Performance and other guarantees not considered for IFRS 9	—	—	(34)
Summary of financial instruments to which the impairment requirements in IFRS 9 are applied/ Summary consolidated income statement	2,350,116	(9,243)	(2,651)
Debt instruments measured at FVOCI	355,664	(166)	(105)
Total allowance for ECL/total income statement ECL change for the period	n/a	(9,409)	(2,756)

Credit quality

Credit quality of financial instruments

(Audited)

We assess the credit quality of all financial instruments that are subject to credit risk. The credit quality of financial instruments is a point-in-time assessment of PD, whereas stages 1 and 2 are determined based on relative deterioration of credit quality since initial recognition. Accordingly, for non-credit-impaired financial

instruments, there is no direct relationship between the credit quality assessment and stages 1 and 2, although typically the lower credit quality bands exhibit a higher proportion in stage 2.

The five credit quality classifications each encompass a range of granular internal credit rating grades assigned to wholesale and personal lending businesses and the external ratings attributed by external agencies to debt securities, as shown in the table on page 163.


Distribution of financial instruments by credit quality at 31 December 2020
(Audited)
	Gross carrying/notional amount						Allowance for ECL/other credit provisions	Net
	Strong	Good	Satisfactory	Sub-standard	Credit impaired	Total
	$m	$m	$m	$m	$m	$m	$m	$m
In-scope for IFRS 9
Loans and advances to customers held at amortised cost	506,231	233,320	256,584	36,970	19,372	1,052,477	(14,490)	1,037,987
– personal	357,821	53,892	38,520	4,965	5,611	460,809	(4,731)	456,078
– corporate and commercial	120,971	158,601	203,560	30,718	13,238	527,088	(9,494)	517,594
– non-bank financial institutions	27,439	20,827	14,504	1,287	523	64,580	(265)	64,315
Loans and advances to banks held at amortised cost	71,318	5,496	3,568	1,276	—	81,658	(42)	81,616
Cash and balances at central banks	302,028	1,388	1,070	—	—	304,486	(5)	304,481
Items in the course of collection from other banks	4,079	9	6	—	—	4,094	—	4,094
Hong Kong Government certificates of indebtedness	40,420	—	—	—	—	40,420	—	40,420
Reverse repurchase agreements – non-trading	177,457	40,461	12,398	312	—	230,628	—	230,628
Financial investments	77,361	9,781	1,537	1	39	88,719	(80)	88,639
Prepayments, accrued income and other assets	81,886	10,129	11,570	298	178	104,061	(90)	103,971
– endorsements and acceptances	1,458	4,355	4,245	229	20	10,307	(30)	10,277
– accrued income and other	80,428	5,774	7,325	69	158	93,754	(60)	93,694
Debt instruments measured at fair value through other comprehensive income1	367,685	12,678	10,409	825	306	391,903	(141)	391,762
Out-of-scope for IFRS 9
Trading assets	117,972	14,694	20,809	829	43	154,347	—	154,347
Other financial assets designated and otherwise mandatorily measured at fair value through profit or loss	6,440	2,378	1,827	109	—	10,754	—	10,754
Derivatives	243,005	54,581	8,709	1,359	72	307,726	—	307,726

Total gross carrying amount on balance sheet	1,995,882	384,915	328,487	41,979	20,010	2,771,273	(14,848)	2,756,425
Percentage of total credit quality	72.0%	13.9%	11.9%	1.5%	0.7%	100%
Loan and other credit-related commitments	400,911	157,339	90,784	9,668	1,081	659,783	(734)	659,049


Financial guarantees	6,356	5,194	5,317	1,247	270	18,384	(125)	18,259
In-scope: Irrevocable loan commitments and financial guarantees	407,267	162,533	96,101	10,915	1,351	678,167	(859)	677,308
Loan and other credit-related commitments	59,392	62,664	59,666	2,837	430	184,989	—	184,989
Performance and other guarantees	26,082	27,909	21,256	2,112	755	78,114	(226)	77,888
Out-of-scope: Revocable loan commitments and non-financial guarantees	85,474	90,573	80,922	4,949	1,185	263,103	(226)	262,877

1 For the purposes of this disclosure, gross carrying value is defined as the amortised cost of a financial asset before adjusting for any loss allowance. As such, the gross carrying value of debt instruments at FVOCI as presented above will not reconcile to the balance sheet as it excludes fair value gains and losses.


Distribution of financial instruments by credit quality at 31 December 2019 (continued)
(Audited)
	Gross carrying/notional amount						Allowance for ECL/other credit provisions	Net
	Strong	Good	Satisfactory	Sub- standard	Credit impaired	Total
	$m	$m	$m	$m	$m	$m	$m	$m
In-scope for IFRS 9
Loans and advances to customers held at amortised cost	524,889	258,402	228,485	20,007	13,692	1,045,475	(8,732)	1,036,743
– personal	354,461	45,037	27,636	2,286	4,851	434,271	(3,134)	431,137
– corporate and commercial	138,126	190,470	186,383	16,891	8,629	540,499	(5,438)	535,061
– non-bank financial institutions	32,302	22,895	14,466	830	212	70,705	(160)	70,545
Loans and advances to banks held at amortised cost	60,636	5,329	1,859	1,395	—	69,219	(16)	69,203
Cash and balances at central banks	151,788	1,398	915	—	—	154,101	(2)	154,099
Items in the course of collection from other banks	4,935	18	3	—	—	4,956	—	4,956
Hong Kong Government certificates of indebtedness	38,380	—	—	—	—	38,380	—	38,380
Reverse repurchase agreements – non-trading	193,157	37,947	9,621	137	—	240,862	—	240,862
Financial investments	78,318	6,503	906	61	—	85,788	(53)	85,735
Prepayments, accrued income and other assets	70,675	8,638	11,321	306	152	91,092	(63)	91,029
– endorsements and acceptances	1,133	4,651	4,196	230	4	10,214	(16)	10,198
– accrued income and other	69,542	3,987	7,125	76	148	80,878	(47)	80,831
Debt instruments measured at fair value through other comprehensive income1	333,158	10,966	7,222	544	1	351,891	(166)	351,725
Out-of-scope for IFRS 9
Trading assets	135,059	15,240	22,964	2,181	—	175,444	—	175,444
Other financial assets designated and otherwise mandatorily measured at fair value through profit or loss	4,655	1,391	5,584	139	—	11,769	—	11,769
Derivatives	187,636	42,642	11,894	821	2	242,995	—	242,995

Total gross carrying amount on balance sheet	1,783,286	388,474	300,774	25,591	13,847	2,511,972	(9,032)	2,502,940
Percentage of total credit quality	70.9%	15.5%	12.0%	1.0%	0.6%	100%
Loan and other credit-related commitments	369,424	146,988	77,499	5,338	780	600,029	(329)	599,700


Financial guarantees	7,441	6,033	5,539	1,011	190	20,214	(48)	20,166
In-scope: Irrevocable loan commitments and financial guarantees	376,865	153,021	83,038	6,349	970	620,243	(377)	619,866
Loan and other credit-related commitments	66,148	69,890	58,754	2,605	182	197,579	—	197,579
Performance and other guarantees	30,099	23,335	20,062	2,057	380	75,933	(132)	75,801
Out-of-scope: Revocable loan commitments and non-financial guarantees	96,247	93,225	78,816	4,662	562	273,512	(132)	273,380


Distribution of financial instruments to which the impairment requirements in IFRS 9 are applied, by credit quality and stage allocation
(Audited)
		Gross carrying/notional amount						Allowance for ECL	Net
		Strong	Good	Satisfactory	Sub- standard	Credit impaired	Total
	Footnotes	$m	$m	$m	$m	$m	$m	$m	$m
Loans and advances to customers at amortised cost		506,231	233,320	256,584	36,970	19,372	1,052,477	(14,490)	1,037,987
– stage 1		499,836	199,138	165,507	5,439	—	869,920	(1,974)	867,946
– stage 2		6,395	34,182	91,077	31,531	—	163,185	(4,965)	158,220
– stage 3		—	—	—	—	19,095	19,095	(7,439)	11,656
– POCI		—	—	—	—	277	277	(112)	165
Loans and advances to banks at amortised cost		71,318	5,496	3,568	1,276	—	81,658	(42)	81,616
– stage 1		71,126	5,098	3,357	73	—	79,654	(33)	79,621
– stage 2		192	398	211	1,203	—	2,004	(9)	1,995
– stage 3		—	—	—	—	—	—	—	—
– POCI		—	—	—	—	—	—	—	—
Other financial assets measured at amortised cost		683,231	61,768	26,581	611	217	772,408	(175)	772,233
– stage 1		682,412	61,218	24,532	54	—	768,216	(80)	768,136
– stage 2		819	550	2,049	557	—	3,975	(44)	3,931
– stage 3		—	—	—	—	177	177	(42)	135
– POCI		—	—	—	—	40	40	(9)	31
Loan and other credit-related commitments		400,911	157,339	90,784	9,668	1,081	659,783	(734)	659,049
– stage 1		396,028	143,600	63,592	1,265	—	604,485	(290)	604,195
– stage 2		4,883	13,739	27,192	8,403	—	54,217	(365)	53,852
– stage 3		—	—	—	—	1,080	1,080	(78)	1,002
– POCI		—	—	—	—	1	1	(1)	—
Financial guarantees		6,356	5,194	5,317	1,247	270	18,384	(125)	18,259
– stage 1		6,286	4,431	3,163	210	—	14,090	(37)	14,053
– stage 2		70	763	2,154	1,037	—	4,024	(62)	3,962
– stage 3		—	—	—	—	269	269	(26)	243
– POCI		—	—	—	—	1	1	—	1
At 31 Dec 2020		1,668,047	463,117	382,834	49,772	20,940	2,584,710	(15,566)	2,569,144
Debt instruments at FVOCI	1
– stage 1		367,542	12,585	10,066	—	—	390,193	(88)	390,105
– stage 2		143	93	343	825	—	1,404	(20)	1,384
– stage 3		—	—	—	—	257	257	(23)	234
– POCI		—	—	—	—	49	49	(10)	39
At 31 Dec 2020		367,685	12,678	10,409	825	306	391,903	(141)	391,762


Distribution of financial instruments to which the impairment requirements in IFRS 9 are applied, by credit quality and stage allocation (continued)
(Audited)
		Gross carrying/notional amount
		Strong	Good	Satisfactory	Sub-standard	Credit impaired	Total	Allowance for ECL	Net
	Footnotes	$m	$m	$m	$m	$m	$m	$m	$m
Loans and advances to customers at amortised cost		524,889	258,402	228,485	20,007	13,692	1,045,475	(8,732)	1,036,743
– stage 1		523,092	242,631	181,056	4,804	—	951,583	(1,297)	950,286
– stage 2		1,797	15,771	47,429	15,185	—	80,182	(2,284)	77,898
– stage 3		—	—	—	—	13,378	13,378	(5,052)	8,326
– POCI		—	—	—	18	314	332	(99)	233
Loans and advances to banks at amortised cost		60,636	5,329	1,859	1,395	—	69,219	(16)	69,203
– stage 1		60,548	5,312	1,797	112	—	67,769	(14)	67,755
– stage 2		88	17	62	1,283	—	1,450	(2)	1,448
– stage 3		—	—	—	—	—	—	—	—
– POCI		—	—	—	—	—	—	—	—
Other financial assets measured at amortised cost		537,253	54,505	22,766	503	152	615,179	(118)	615,061
– stage 1		536,942	54,058	21,921	279	—	613,200	(38)	613,162
– stage 2		311	447	845	224	—	1,827	(38)	1,789
– stage 3		—	—	—	—	151	151	(42)	109
– POCI		—	—	—	—	1	1	—	1
Loan and other credit-related commitments		369,424	146,988	77,499	5,338	780	600,029	(329)	599,700
– stage 1		368,711	141,322	66,283	1,315	—	577,631	(137)	577,494
– stage 2		713	5,666	11,216	4,023	—	21,618	(133)	21,485
– stage 3		—	—	—	—	771	771	(59)	712
– POCI		—	—	—	—	9	9	—	9
Financial guarantees		7,441	6,033	5,539	1,011	190	20,214	(48)	20,166
– stage 1		7,400	5,746	4,200	338	—	17,684	(16)	17,668
– stage 2		41	287	1,339	673	—	2,340	(22)	2,318
– stage 3		—	—	—	—	186	186	(10)	176
– POCI		—	—	—	—	4	4	—	4
At 31 Dec 2019		1,499,643	471,257	336,148	28,254	14,814	2,350,116	(9,243)	2,340,873
Debt instruments at FVOCI	1
– stage 1		333,072	10,941	6,902	—	—	350,915	(39)	350,876
– stage 2		86	25	320	544	—	975	(127)	848
– stage 3		—	—	—	—	—	—	—	—
– POCI		—	—	—	—	1	1	—	1
At 31 Dec 2019		333,158	10,966	7,222	544	1	351,891	(166)	351,725

Credit-impaired loans

(Audited)

We determine that a financial instrument is credit impaired and in stage 3 by considering relevant objective evidence, primarily whether:

•contractual payments of either principal or interest are past due for more than 90 days;

•there are other indications that the borrower is unlikely to pay, such as when a concession has been granted to the borrower for economic or legal reasons relating to the borrower’s financial condition; and

•the loan is otherwise considered to be in default. If such unlikeliness to pay is not identified at an earlier stage, it is deemed to occur when an exposure is 90 days past due, even where regulatory rules permit default to be defined based on 180 days past due. Therefore, the definitions of credit impaired and default are aligned as far as possible so that stage 3 represents all loans that are considered defaulted or otherwise credit impaired.


Wholesale lending – reconciliation of changes in gross carrying/nominal amount and allowances for loans and advances to banks and customers including loan commitments and financial guarantees
(Audited)
	Non-credit impaired				Credit impaired
	Stage 1		Stage 2		Stage 3		POCI		Total
	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL
	$m	$m	$m	$m	$m	$m	$m	$m	$m	$m
At 1 Jan 2019	922,192	(902)	78,266	(1,012)	9,239	(3,987)	334	(194)	1,010,031	(6,095)
Transfers of financial instruments	(31,493)	(169)	28,418	276	3,075	(107)	—	—	—	—
Net remeasurement of ECL arising from transfer of stage	—	223	—	(268)	—	(38)	—	—	—	(83)
Net new and further lending/repayments	27,918	(134)	(20,121)	167	(1,552)	369	137	(1)	6,382	401
Changes to risk parameters – credit quality	—	102	—	(193)	—	(1,514)	—	(51)	—	(1,656)
Changes to models used for ECL calculation	—	—	—	(56)	—	—	—	—	—	(56)
Assets written off	—	—	—	—	(1,312)	1,312	(140)	140	(1,452)	1,452
Credit-related modifications that resulted in derecognition	—	—	—	—	(268)	125	—	—	(268)	125
Foreign exchange and other	7,035	13	1,606	(17)	107	(66)	14	7	8,762	(63)
At 31 Dec 2019	925,652	(867)	88,169	(1,103)	9,289	(3,906)	345	(99)	1,023,455	(5,975)
ECL income statement change for the period		191		(350)		(1,183)		(52)		(1,394)
Recoveries										47
Others										(24)
Total ECL income statement change for the period										(1,371)

Collateral and other credit enhancements

(Audited)

Although collateral can be an important mitigant of credit risk, it is the Group’s practice to lend on the basis of the customer’s ability to meet their obligations out of cash flow resources rather than placing primary reliance on collateral and other credit risk enhancements. Depending on the customer’s standing and the type of product, facilities may be provided without any collateral or other credit enhancements. For other lending, a charge over collateral is obtained and considered in determining the credit decision and pricing. In the event of default, the Group may utilise the collateral as a source of repayment.

Depending on its form, collateral can have a significant financial effect in mitigating our exposure to credit risk. Where there is sufficient collateral, an expected credit loss is not recognised. This is the case for reverse repurchase agreements and for certain loans and advances to customers where the loan to value (‘LTV’) is very low.

Mitigants may include a charge on borrowers’ specific assets, such as real estate or financial instruments. Other credit risk mitigants include short positions in securities and financial assets held as part of linked insurance/investment contracts where the risk is predominantly borne by the policyholder. Additionally, risk may be managed by employing other types of collateral and credit risk enhancements, such as second charges, other liens and

unsupported guarantees. Guarantees are normally taken from corporates and export credit agencies. Corporates would normally provide guarantees as part of a parent/subsidiary relationship and span a number of credit grades. The export credit agencies will normally be investment grade.

Certain credit mitigants are used strategically in portfolio management activities. While single name concentrations arise in portfolios managed by Global Banking and Corporate Banking, it is only in Global Banking that their size requires the use of portfolio level credit mitigants. Across Global Banking, risk limits and utilisations, maturity profiles and risk quality are monitored and managed proactively. This process is key to the setting of risk appetite for these larger, more complex, geographically distributed customer groups. While the principal form of risk management continues to be at the point of exposure origination, through the lending decision-making process, Global Banking also utilises loan sales and credit default swap (‘CDS’) hedges to manage concentrations and reduce risk. These transactions are the responsibility of a dedicated Global Banking portfolio management team. Hedging activity is carried out within agreed credit parameters, and is subject to market risk limits and a robust governance structure. Where applicable, CDSs are entered into directly with a central clearing house counterparty. Otherwise, the Group’s exposure to CDS protection providers is diversified among mainly banking counterparties with strong credit ratings.

CDS mitigants are held at portfolio level and are not included in the expected loss calculations. CDS mitigants are not reported in the following tables.

Collateral on loans and advances

Collateral held is analysed separately for commercial real estate and for other corporate, commercial and financial (non-bank) lending. The following tables include off-balance sheet loan commitments, primarily undrawn credit lines.

The collateral measured in the following tables consists of fixed first charges on real estate, and charges over cash and marketable financial instruments. The values in the tables represent the expected market value on an open market basis. No adjustment has been made to the collateral for any expected costs of recovery. Marketable securities are measured at their fair value.

Other types of collateral such as unsupported guarantees and floating charges over the assets of a customer’s business are not measured in the following tables. While such mitigants have value, often providing rights in insolvency, their assignable value is not sufficiently certain and they are therefore assigned no value for disclosure purposes.

The LTV ratios presented are calculated by directly associating loans and advances with the collateral that individually and uniquely supports each facility. When collateral assets are shared by multiple loans and advances, whether specifically or, more generally, by way of an all monies charge, the collateral value is pro-rated across the loans and advances protected by the collateral.

For credit-impaired loans, the collateral values cannot be directly compared with impairment allowances recognised. The LTV figures use open market values with no adjustments. Impairment allowances are calculated on a different basis, by considering other cash flows and adjusting collateral values for costs of realising collateral as explained further on page 330.

Commercial real estate loans and advances

The value of commercial real estate collateral is determined by using a combination of external and internal valuations and physical inspections. For CRR 1–7, local valuation policies determine the frequency of review on the basis of local market conditions because of the complexity of valuing collateral for commercial real estate. For CRR 8–10, almost all collateral would have been revalued within the last three years.

In Hong Kong, market practice is typically for lending to major property companies to be either secured by guarantees or unsecured. In Europe, facilities of a working capital nature are generally not secured by a first fixed charge, and are therefore disclosed as not collateralised.


Personal lending – reconciliation of changes in gross carrying/nominal amount and allowances for loans and advances to customers including loan commitments and financial guarantees
(Audited)
	Non-credit impaired				Credit impaired
	Stage 1		Stage 2		Stage 3		Total
	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL
	$m	$m	$m	$m	$m	$m	$m	$m
At 1 Jan 2020	635,961	(597)	17,382	(1,338)	5,046	(1,215)	658,389	(3,150)
Transfers of financial instruments	(16,019)	(629)	13,370	1,181	2,649	(552)	—	—




Net remeasurement of ECL arising from transfer of stage	—	431	—	(555)	—	(8)	—	(132)




Net new and further lending/repayments	30,891	101	(5,407)	408	(677)	150	24,807	659
Change in risk parameters – credit quality	—	(147)	—	(2,025)	—	(1,258)	—	(3,430)
Changes to models used for ECL calculation	—	(3)	—	(9)	—	5	—	(7)
Assets written off	—	—	—	—	(1,409)	1,407	(1,409)	1,407

Foreign exchange and other	14,513	(22)	1,425	(67)	153	(32)	16,091	(121)


At 31 Dec 2020	665,346	(866)	26,770	(2,405)	5,762	(1,503)	697,878	(4,774)
ECL income statement change for the period		382		(2,181)		(1,111)		(2,910)
Recoveries								280



Other								(25)
Total ECL income statement change for the period								(2,655)


Personal lending – reconciliation of changes in gross carrying/nominal amount and allowances for loans and advances to customers including loan commitments and financial guarantees (continued)
(Audited)
	Non-credit impaired				Credit impaired
	Stage 1		Stage 2		Stage 3		Total
	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL	Gross carrying/ nominal amount	Allowance for ECL
	$m	$m	$m	$m	$m	$m	$m	$m
At 1 Jan 2019	580,784	(547)	16,838	(1,266)	4,993	(1,148)	602,615	(2,961)
Transfers of financial instruments	(4,751)	(374)	2,645	858	2,106	(484)	—	—
Net remeasurement of ECL arising from transfer of stage	—	446	—	(408)	—	(76)	—	(38)
Net new and further lending/repayments	50,946	3	(2,348)	453	(758)	281	47,840	737
Change in risk parameters – credit quality	—	(100)	—	(1,015)	—	(1,190)	—	(2,305)
Changes to models used for ECL calculation	—	(6)	—	60	—	14	—	68
Assets written off	—	—	—	—	(1,345)	1,345	(1,345)	1,345
Foreign exchange and other	8,982	(19)	247	(20)	50	43	9,279	4
At 31 Dec 2019	635,961	(597)	17,382	(1,338)	5,046	(1,215)	658,389	(3,150)
ECL income statement change for the period		343		(910)		(971)		(1,538)
Recoveries								314
Other								4
Total ECL income statement change for the period								(1,220)

Collateral on loans and advances

(Audited)

The following table provides a quantification of the value of fixed charges we hold over specific assets where we have a history of enforcing, and are able to enforce, collateral in satisfying a debt in the event of the borrower failing to meet its contractual

obligations, and where the collateral is cash or can be realised by sale in an established market. The collateral valuation excludes any adjustments for obtaining and selling the collateral and, in particular, loans shown as not collateralised or partially collateralised may also benefit from other forms of credit mitigants.

HSBC Holdings

(Audited)

Risk in HSBC Holdings is overseen by the HSBC Holdings Asset and Liability Management Committee (‘Holdings ALCO’). The major risks faced by HSBC Holdings are credit risk, liquidity risk and market risk (in the form of interest rate risk and foreign exchange risk).

Credit risk in HSBC Holdings primarily arises from transactions with Group subsidiaries and its investments in those subsidiaries.

In HSBC Holdings, the maximum exposure to credit risk arises from two components:

•financial instruments on the balance sheet (see page 321); and

•financial guarantees and similar contracts, where the maximum exposure is the maximum that we would have to pay if the guarantees were called upon (see Note 32).

In the case of our derivative balances, we have amounts with a legally enforceable right of offset in the case of counterparty default that are not included in the carrying value. These offsets also include collateral received in cash and other financial assets.

The total offset relating to our derivative balances was $1.7bn at 31 December 2020 (2019: $0.1bn).

The credit quality of loans and advances and financial investments, both of which consist of intra-Group lending and US Treasury bills and bonds, is assessed as ‘strong’, with 100% of the exposure being neither past due nor impaired (2019: 100%). For further details of credit quality classification, see page 163. risk management

Own funds


Own funds disclosure
(Audited)
		At
		31 Dec	31 Dec
		2020	2019
Ref*		$m	$m
	Common equity tier 1 (‘CET1’) capital: instruments and reserves
1	Capital instruments and the related share premium accounts	23,219	22,873
	– ordinary shares	23,219	22,873
2	Retained earnings	128,665	127,188
3	Accumulated other comprehensive income (and other reserves)1	9,768	1,735
5	Minority interests (amount allowed in consolidated CET1)	4,079	4,865
5a	Independently reviewed interim net profits net of any foreseeable charge or dividend	(252)	(3,381)
6	Common equity tier 1 capital before regulatory adjustments	165,479	153,280










28	Total regulatory adjustments to common equity tier 1	(29,429)	(29,314)
29	Common equity tier 1 capital	136,050	123,966






36	Additional tier 1 capital before regulatory adjustments	24,183	24,453




43	Total regulatory adjustments to additional tier 1 capital	(60)	(60)
44	Additional tier 1 capital	24,123	24,393
45	Tier 1 capital	160,173	148,359







51	Tier 2 capital before regulatory adjustments	25,722	25,192



57	Total regulatory adjustments to tier 2 capital	(1,472)	(1,401)
58	Tier 2 capital	24,250	23,791
59	Total capital	184,423	172,150

* The references identify the lines prescribed in the European Banking Authority (‘EBA’) template, which are applicable and where there is a value.

1Following the call and subsequent redemption of HSBC Holdings' non-cumulative preference shares, the remaining share premium that related to such preference shares is now treated as an 'other reserve' and included in CET1.

Throughout 2020, we complied with the PRA’s regulatory capital adequacy requirements, including those relating to stress testing. At 31 December 2020, our CET1 ratio increased to 15.9% from 14.7% at 31 December 2019.

CET1 capital increased during the year by $12.1bn, mainly as a result of:

•the cancellation of the fourth interim dividend of $3.4bn for 2019;

•favourable foreign currency translation differences of $3.4bn;

•capital generation of $2.8bn net of dividends relating to other equity instruments;

•a fall of $2.1bn in the deduction for other intangible assets due to changes to the capital treatment of software assets;

•a $1.8bn increase in fair value through other comprehensive income reserve; and

•a $1.8bn fall in the deduction for excess expected loss.

These increases were partly offset by:

•an interim dividend for 2020 of $3.1bn; and

•a $0.8bn fall in allowable non-controlling interest in CET1. This partly reflected the acquisition in May 2020 of additional shares representing 18.66% of the capital of HSBC Trinkaus and Burkhardt from Landesbank Baden-Württemberg, the principal minority shareholder.

We have applied the revised regulatory treatment of software assets, which became a UK requirement in December 2020. Subsequently, the PRA announced its intention to consult on a reversal of this change in due course and recommended firms do not base their distribution decision on any capital increase from applying this requirement. As a result, we have not considered the related capital benefit in our distributions. The impact of the change on our CET1 ratio was 0.2 percentage points.

Our Pillar 2A requirement at 31 December 2020, as per the PRA’s Individual Capital Requirement based on a point-in-time assessment, was equivalent to 3.0% of RWAs, of which 1.7% was met by CET1.

Value at risk

(Audited)

VaR is a technique for estimating potential losses on risk positions as a result of movements in market rates and prices over a specified time horizon and to a given level of confidence. The use of VaR is integrated into market risk management and calculated for all trading positions regardless of how we capitalise them. In addition, we calculate VaR for non-trading portfolios to have a complete picture of risk. Where we do not calculate VaR explicitly, we use alternative tools as summarised in the ‘Stress testing’ section below.

Our models are predominantly based on historical simulation that incorporates the following features:

•historical market rates and prices, which are calculated with reference to foreign exchange rates, commodity prices, interest rates, equity prices and the associated volatilities;

•potential market movements that are calculated with reference to data from the past two years; and

•calculations to a 99% confidence level and using a one-day holding period.

The models also incorporate the effect of option features on the underlying exposures. The nature of the VaR models means that an increase in observed market volatility will lead to an increase in VaR without any changes in the underlying positions.

VaR model limitations

Although a valuable guide to risk, VaR is used with awareness of its limitations. For example:

•The use of historical data as a proxy for estimating future market moves may not encompass all potential market events, particularly those that are extreme in nature.

•The use of a one-day holding period for risk management purposes of trading and non-trading books assumes that this short period is sufficient to hedge or liquidate all positions.

•The use of a 99% confidence level by definition does not take into account losses that might occur beyond this level of confidence.

•VaR is calculated on the basis of exposures outstanding at the close of business and therefore does not reflect intra-day exposures.

Risk not in VaR framework

The risks not in VaR (‘RNIV’) framework captures and capitalises material market risks that are not adequately covered in the VaR model.

Risk factors are reviewed on a regular basis and are either incorporated directly in the VaR models, where possible, or quantified through either the VaR-based RNIV approach or a stress test approach within the RNIV framework. While VaR-based RNIVs are calculated by using historical scenarios, stress-type RNIVs are estimated on the basis of stress scenarios whose severity is calibrated to be in line with the capital adequacy requirements. The outcome of the VaR-based RNIV approach is included in the overall VaR calculation but excluded from the VaR measure used for regulatory back-testing. In addition, the stressed VaR measure also includes risk factors considered in the VaR-based RNIV approach.

Stress-type RNIVs include a deal contingent derivatives capital charge to capture risk for these transactions and a de-peg risk measure to capture risk to pegged and heavily managed currencies.

Stress testing

Stress testing is an important procedure that is integrated into our market risk management framework to evaluate the potential impact on portfolio values of more extreme, although plausible, events or movements in a set of financial variables. In such scenarios, losses can be much greater than those predicted by VaR modelling.

Stress testing is implemented at legal entity, regional and overall Group levels. A set of scenarios is used consistently across all regions within the Group. The risk appetite around potential stress losses for the Group is set and monitored against a referral limit.

Market risk reverse stress tests are designed to identify vulnerabilities in our portfolios by looking for scenarios that lead to loss levels considered severe for the relevant portfolio. These scenarios may be quite local or idiosyncratic in nature, and complement the systematic top-down stress testing.

Stress testing and reverse stress testing provide senior management with insights regarding the ‘tail risk’ beyond VaR, for which our appetite is limited.

The Group non-trading VaR for the year is shown in the table below.


Non-trading VaR, 99% 1 day
(Audited)
	Interest rate	Credit spread	Portfolio diversification1	Total2
	$m	$m	$m	$m
Balance at 31 Dec 2020	166.6	87.0	(5.7)	247.8
Average	150.2	82.5	(42.0)	190.7
Maximum	196.4	133.4	—	274.6
Minimum	59.0	44.2	—	79.7

Balance at 31 Dec 2019	96.2	62.5	(28.2)	130.5
Average	65.9	44.2	(25.6)	84.5
Maximum	100.1	81.2	0	132.8
Minimum	49.2	26.6	0	60.9

1 Portfolio diversification is the market risk dispersion effect of holding a portfolio containing different risk types. It represents the reduction in unsystematic market risk that occurs when combining a number of different risk types – such as interest rate, equity and foreign exchange – together in one portfolio. It is measured as the difference between the sum of the VaR by individual risk type and the combined total VaR. A negative number represents the benefit of portfolio diversification. As the maximum and minimum occurs on different days for different risk types, it is not meaningful to calculate a portfolio diversification benefit for these measures.

2 The total VaR is non-additive across risk types due to diversification effects.

Governance and structure

(Audited)

Insurance risks are managed to a defined risk appetite, which is aligned to the Group’s risk appetite and risk management framework, including its three lines of defence model. For details of the Group’s governance framework, see page 132. The Global Insurance Risk Management Meeting oversees the control framework globally and is accountable to the WPB Risk Management Meeting on risk matters relating to the insurance business.

The monitoring of the risks within our insurance operations is carried out by insurance risk teams. Specific risk functions, including Wholesale Credit and Market Risk, Operational and Resilience Risk, and Compliance, support Insurance Risk teams in their respective areas of expertise.

Stress and scenario testing

(Audited)

Stress testing forms a key part of the risk management framework for the insurance business. We participate in local and Group-wide regulatory stress tests, as well as internally-developed stress and scenario tests, including Group internal stress test exercises.

These have highlighted that a key risk scenario for the insurance business is a prolonged low interest-rate environment. In order to mitigate the impact of this scenario, the insurance operations have taken a number of actions, including repricing some products to reflect lower interest rates, launching less capital intensive products, investing in more capital efficient assets and developing investment strategies to optimise the expected returns against the cost of economic capital.

Key risk management processes

Market risk

(Audited)

All our insurance manufacturing subsidiaries have market risk mandates that specify the investment instruments in which they are permitted to invest and the maximum quantum of market risk that they may retain. They manage market risk by using, among others, some or all of the techniques listed below, depending on the nature of the contracts written:

•We are able to adjust bonus rates to manage the liabilities to policyholders for products with discretionary participating features (‘DPF’). The effect is that a significant portion of the market risk is borne by the policyholder.

•We use asset and liability matching where asset portfolios are structured to support projected liability cash flows. The Group manages its assets using an approach that considers asset quality, diversification, cash flow matching, liquidity, volatility and target investment return. It is not always possible to match asset and liability durations due to uncertainty over the receipt of all future premiums, the timing of claims and because the forecast payment dates of liabilities may exceed the duration of the longest dated investments available. We use models to assess the effect of a range of future scenarios on the values of financial assets and associated liabilities, and ALCOs employ the outcomes in determining how best to structure asset holdings to support liabilities.

•We use derivatives to protect against adverse market movements to better match liability cash flows.

•For new products with investment guarantees, we consider the cost when determining the level of premiums or the price structure.

•We periodically review products identified as higher risk, such as those that contain investment guarantees and embedded optionality features linked to savings and investment products, for active management.

•We design new products to mitigate market risk, such as changing the investment return sharing portion between policyholders and the shareholder.

•We exit, to the extent possible, investment portfolios whose risk is considered unacceptable.

•We reprice premiums charged on new contracts to policyholders.

Credit risk

(Audited)

Our insurance manufacturing subsidiaries are responsible for the credit risk, quality and performance of their investment portfolios. Our assessment of the creditworthiness of issuers and counterparties is based primarily upon internationally recognised credit ratings and other publicly available information.

Investment credit exposures are monitored against limits by our insurance manufacturing subsidiaries and are aggregated and reported to the Group Insurance Credit Risk and Group Credit Risk functions. Stress testing is performed on investment credit exposures using credit spread sensitivities and default probabilities.

We use a number of tools to manage and monitor credit risk. These include a credit report containing a watch-list of investments with current credit concerns, primarily investments that may be at risk of future impairment or where high concentrations to counterparties are present in the investment portfolio. Sensitivities to credit spread risk are assessed and monitored regularly.

Liquidity risk

(Audited)

Risk is managed by cash flow matching and maintaining sufficient cash resources, investing in high credit-quality investments with deep and liquid markets, monitoring investment concentrations and restricting them where appropriate, and establishing committed contingency borrowing facilities.

Insurance manufacturing subsidiaries complete quarterly liquidity risk reports and an annual review of the liquidity risks to which they are exposed.

Measurement

(Audited)

The risk profile of our insurance manufacturing businesses is measured using an economic capital approach. Assets and liabilities are measured on a market value basis, and a capital requirement is defined to ensure that there is a less than one-in-200 chance of insolvency over a one-year time horizon, given the risks to which the businesses are exposed. The methodology for the economic capital calculation is largely aligned to the pan-European Solvency II insurance capital regulations. The economic capital coverage ratio (economic net asset value divided by the economic capital requirement) is a key risk appetite measure.

The Covid-19 outbreak caused sales of insurance products to be lower than forecast in 2020, although we responded by expanding digital and remote servicing capabilities. To date there has been limited impact on claims or lapse behaviours, although this remains under close monitoring. The largest effect on insurance entities came from volatility in the financial markets and the material fall in interest rates, which impact levels of capital and profitability. Businesses responded by executing de-risking strategies followed by subsequent re-risking of positions as markets recovered. Enhanced monitoring of risks and pricing conditions continues.

The following tables show the composition of assets and liabilities by contract type and by geographical region.


Balance sheet of insurance manufacturing subsidiaries by type of contract1
(Audited)
		With DPF	Unit-linked	Other contracts2	Shareholder assets and liabilities	Total
	Footnotes	$m	$m	$m	$m	$m
Financial assets		84,478	8,802	18,932	8,915	121,127
– trading assets		—	—	—	—	—
– financial assets designated and otherwise mandatorily measured at fair value through profit or loss		26,002	8,558	3,508	1,485	39,553
– derivatives		262	3	13	3	281
– financial investments at amortised cost		39,891	30	13,984	4,521	58,426
– financial investments at fair value through other comprehensive income		12,531	—	459	1,931	14,921
– other financial assets	3	5,792	211	968	975	7,946
Reinsurance assets		2,256	65	1,447	2	3,770
PVIF	4	—	—	—	9,435	9,435
Other assets and investment properties		2,628	1	227	721	3,577
Total assets		89,362	8,868	20,606	19,073	137,909
Liabilities under investment contracts designated at fair value		—	2,285	4,100	—	6,385
Liabilities under insurance contracts		84,931	6,503	15,827	—	107,261
Deferred tax	5	145	5	25	1,400	1,575
Other liabilities		—	—	—	7,244	7,244
Total liabilities		85,076	8,793	19,952	8,644	122,465
Total equity		—	—	—	15,444	15,444
Total liabilities and equity at 31 Dec 2020		85,076	8,793	19,952	24,088	137,909











Financial assets		73,929	7,333	17,514	8,269	107,045
– trading assets		—	—	—	—	—
– financial assets designated and otherwise mandatorily measured at fair value through profit or loss		21,652	7,119	3,081	2,426	34,278
– derivatives		202	(6)	9	3	208
– financial investments at amortised cost		35,299	18	13,436	4,076	52,829
– financial investments at fair value through other comprehensive income		12,447	—	445	1,136	14,028
– other financial assets	3	4,329	202	543	628	5,702
Reinsurance assets		2,208	72	1,563	1	3,844
PVIF	4	—	—	—	8,945	8,945
Other assets and investment properties		2,495	2	211	602	3,310
Total assets		78,632	7,407	19,288	17,817	123,144
Liabilities under investment contracts designated at fair value		—	2,011	3,881	—	5,892
Liabilities under insurance contracts		77,147	6,151	14,141	—	97,439
Deferred tax	5	197	23	6	1,297	1,523
Other liabilities		—	—	—	4,410	4,410
Total liabilities		77,344	8,185	18,028	5,707	109,264
Total equity		—	—	—	13,879	13,879
Total liabilities and equity at 31 Dec 2019		77,344	8,185	18,028	19,586	123,143

1Balance sheet of insurance manufacturing operations are shown before elimination of inter-company transactions with HSBC non-insurance operations.

2‘Other Contracts’ includes term insurance, credit life insurance, universal life insurance and investment contracts not included in the ‘Unit-linked’ or ‘With DPF’ columns.

3Comprise mainly loans and advances to banks, cash and inter-company balances with other non-insurance legal entities.

4Present value of in-force long-term insurance business.

5‘Deferred tax’ includes the deferred tax liabilities arising on recognition of PVIF.


Balance sheet of insurance manufacturing subsidiaries by geographical region1,2
(Audited)
		Europe	Asia	Latin America	Total
	Footnotes	$m	$m	$m	$m
Financial assets		34,768	85,259	1,100	121,127
– trading assets		—	—	—	—
– financial assets designated and otherwise mandatorily measured at fair value through profit or loss		17,184	22,099	270	39,553
– derivatives		107	174	—	281
– financial investments – at amortised cost		531	57,420	475	58,426
– financial investments – at fair value through other comprehensive income		13,894	706	321	14,921
– other financial assets	3	3,052	4,860	34	7,946
Reinsurance assets		245	3,521	4	3,770
PVIF	4	884	8,390	161	9,435
Other assets and investment properties		1,189	2,332	56	3,577
Total assets		37,086	99,502	1,321	137,909
Liabilities under investment contracts designated at fair value		1,288	5,097	—	6,385
Liabilities under insurance contracts		31,153	74,994	1,114	107,261
Deferred tax	5	204	1,348	23	1,575
Other liabilities		2,426	4,800	18	7,244
Total liabilities		35,071	86,239	1,155	122,465
Total equity		2,015	13,263	166	15,444
Total liabilities and equity at 31 Dec 2020		37,086	99,502	1,321	137,909











Financial assets		31,613	74,237	1,195	107,045
– trading assets		—	—	—	—
– financial assets designated and otherwise mandatorily measured at fair value through profit or loss		15,490	18,562	226	34,278
– derivatives		84	124	—	208
– financial investments – at amortised cost		100	52,186	543	52,829
– financial investments – at fair value through other comprehensive income		13,071	582	375	14,028
– other financial assets	3	2,868	2,783	51	5,702
Reinsurance assets		237	3,604	3	3,844
PVIF	4	945	7,841	159	8,945
Other assets and investment properties		1,085	2,176	49	3,310
Total assets		33,880	87,858	1,406	123,144
Liabilities under investment contracts designated at fair value		1,139	4,753	—	5,892
Liabilities under insurance contracts		28,437	67,884	1,118	97,439
Deferred tax	5	229	1,275	19	1,523
Other liabilities		2,212	2,172	26	4,410
Total liabilities		32,017	76,084	1,163	109,264
Total equity		1,862	11,774	243	13,879
Total liabilities and equity at 31 Dec 2019		33,879	87,858	1,406	123,143

1HSBC has no insurance manufacturing subsidiaries in the Middle East and North Africa or North America.

2Balance sheet of insurance manufacturing operations are shown before elimination of inter-company transactions with HSBC non-insurance operations.

3Comprise mainly loans and advances to banks, cash and inter-company balances with other non-insurance legal entities.

4Present value of in-force long-term insurance business.

5‘Deferred tax’ includes the deferred tax liabilities arising on recognition of PVIF.

Market risk

(Audited)

Description and exposure

Market risk is the risk of changes in market factors affecting HSBC’s capital or profit. Market factors include interest rates, equity and growth assets and foreign exchange rates.

Our exposure varies depending on the type of contract issued. Our most significant life insurance products are contracts with discretionary participating features (‘DPF’) issued in France and Hong Kong. These products typically include some form of capital guarantee or guaranteed return on the sums invested by the policyholders, to which discretionary bonuses are added if allowed by the overall performance of the funds. These funds are primarily

invested in bonds, with a proportion allocated to other asset classes to provide customers with the potential for enhanced returns.

DPF products expose HSBC to the risk of variation in asset returns, which will impact our participation in the investment performance.

In addition, in some scenarios the asset returns can become insufficient to cover the policyholders’ financial guarantees, in which case the shortfall has to be met by HSBC. Amounts are held against the cost of such guarantees, calculated by stochastic modelling.

The cost of such guarantees is accounted for as a deduction from the present value of in-force ('PVIF') asset, unless the cost of such guarantees is already explicitly allowed for within the insurance contract liabilities under the local rules.

The following table shows the total reserve held for the cost of guarantees, the range of investment returns on assets supporting these products and the implied investment return that would enable the business to meet the guarantees.

The cost of guarantees increased to $1,105m (2019: $693m) primarily due to the reduction in swap rates in France and Hong

Kong, partly offset by the impact of modelling changes in France and Hong Kong.

For unit-linked contracts, market risk is substantially borne by the

policyholder, but some market risk exposure typically remains, as fees earned are related to the market value of the linked assets.


Financial return guarantees
(Audited)
	2020			2019
	Investment returns implied by guarantee	Long-term investment returns on relevant portfolios	Cost of guarantees	Investment returns implied by guarantee	Long-term investment returns on relevant portfolios	Cost of guarantees
	%	%	$m	%	%	$m
Capital	0.0	0.7–3.2	277	0.0	1.3–3.9	110
Nominal annual return	0.1–1.9	2.3–3.6	515	0.1–2.0	3.0–4.5	118
Nominal annual return	2.0-3.9	2.0–4.5	180	2.0–4.0	2.4–4.5	355
Nominal annual return	4.0–5.0	2.0–4.2	133	4.1–5.0	2.3–4.1	110
At 31 Dec			1,105			693


Sensitivity of HSBC’s insurance manufacturing subsidiaries to market risk factors
(Audited)
	2020		2019
	Effect on profit after tax	Effect on total equity	Effect on profit after tax	Effect on total equity
	$m	$m	$m	$m
+100 basis point parallel shift in yield curves	(67)	(188)	43	(37)
-100 basis point parallel shift in yield curves	(68)	58	(221)	(138)
10% increase in equity prices	332	332	270	270
10% decrease in equity prices	(338)	(338)	(276)	(276)
10% increase in US dollar exchange rate compared with all currencies	84	84	41	41
10% decrease in US dollar exchange rate compared with all currencies	(84)	(84)	(41)	(41)

Credit risk

(Audited)

Description and exposure

Credit risk is the risk of financial loss if a customer or counterparty fails to meet their obligation under a contract. It arises in two main areas for our insurance manufacturers:

•risk associated with credit spread volatility and default by debt security counterparties after investing premiums to generate a return for policyholders and shareholders; and

•risk of default by reinsurance counterparties and non-reimbursement for claims made after ceding insurance risk.

The amounts outstanding at the balance sheet date in respect of these items are shown in the table on page 233.

The credit quality of the reinsurers’ share of liabilities under insurance contracts is assessed as ‘satisfactory’ or higher (as defined on page 163), with 100% of the exposure being neither past due nor impaired (2019: 100%).

Credit risk on assets supporting unit-linked liabilities is predominantly borne by the policyholder. Therefore, our exposure is primarily related to liabilities under non-linked insurance and

investment contracts and shareholders’ funds. The credit quality of insurance financial assets is included in the table on page 180.

The risk associated with credit spread volatility is to a large extent mitigated by holding debt securities to maturity, and sharing a degree of credit spread experience with policyholders.

Capital and liquidity risk

(Audited)

Description and exposure

Liquidity risk is the risk that an insurance operation, though solvent, either does not have sufficient financial resources available to meet its obligations when they fall due, or can secure them only at excessive cost.

The following table shows the expected undiscounted cash flows for insurance liabilities at 31 December 2020. The liquidity risk exposure is wholly borne by the policyholder in the case of unit-linked business and is shared with the policyholder for non-linked insurance.

The profile of the expected maturity of insurance contracts at 31 December 2020 remained comparable with 2019.

The remaining contractual maturity of investment contract liabilities is included in Note 29 on page 382.


Expected maturity of insurance contract liabilities
(Audited)
	Expected cash flows (undiscounted)
	Within 1 year	1-5 years	5-15 years	Over 15 years	Total
	$m	$m	$m	$m	$m
Unit-linked	1,407	3,097	2,976	2,099	9,579
With DPF and Other contracts	8,427	30,156	51,383	75,839	165,805
At 31 Dec 2020	9,834	33,253	54,359	77,938	175,384

Unit-linked	1,296	3,153	2,654	1,955	9,058
With DPF and Other contracts	7,907	26,906	50,576	71,731	157,120
At 31 Dec 2019	9,203	30,059	53,230	73,686	166,178

Sensitivities

(Audited)

The following table shows the sensitivity of profit and total equity to reasonably possible changes in non-economic assumptions across all our insurance manufacturing subsidiaries.

Mortality and morbidity risk is typically associated with life insurance contracts. The effect on profit of an increase in mortality or morbidity depends on the type of business being written. Our largest exposures to mortality and morbidity risk exist in Hong Kong.

Sensitivity to lapse rates depends on the type of contracts being written. For a portfolio of term assurance, an increase in lapse rates typically has a negative effect on profit due to the loss of future income on the lapsed policies. However, some contract lapses have a positive effect on profit due to the existence of policy surrender charges. We are most sensitive to a change in lapse rates on unit-linked and universal life contracts in Hong Kong and DPF contracts in France.

Expense rate risk is the exposure to a change in the cost of administering insurance contracts. To the extent that increased expenses cannot be passed on to policyholders, an increase in expense rates will have a negative effect on our profits.


Sensitivity analysis
(Audited)
	2020	2019
	$m	$m
Effect on profit after tax and total equity at 31 Dec
Effect on profit after tax and total equity at 10% increase in mortality and/or morbidity rates	(93)	(88)
Effect on profit after tax and total equity at 10% decrease in mortality and/or morbidity rates	98	88
Effect on profit after tax and total equity at 10% increase in lapse rates	(111)	(99)
Effect on profit after tax and total equity at 10% decrease in lapse rates	128	114
Effect on profit after tax and total equity at 10% increase in expense rates	(117)	(106)
Effect on profit after tax and total equity at 10% decrease in expense rates	115	105