FORM 6-K
 
SECURITIES AND EXCHANGE COMMISSION
 
Washington, D.C. 20549
 
 
 
Report of Foreign Private Issuer
 
Pursuant to Rule 13a - 16 or 15d - 16 of
 
the Securities Exchange Act of 1934
 
 
 
For the month of February
 
HSBC Holdings plc
 
42nd Floor, 8 Canada Square, London E14 5HQ, England
 
(Indicate by check mark whether the registrant files or will file annual reports under cover of Form 20-F or Form 40-F).
 
Form 20-F X Form 40-F  
 
(Indicate by check mark whether the registrant by furnishing the information contained in this Form is also thereby furnishing the information to the Commission pursuant to Rule 12g3-2(b) under the Securities Exchange Act of 1934).
 
Yes  No X
 
(If "Yes" is marked, indicate below the file number assigned to the registrant in connection with Rule 12g3-2(b): 82-   ).
 
 
 
 
 
 
 
 
 
 
 
Click on, or paste the following link into your web browser, to view the associated PDF document.
 
http://www.rns-pdf.londonstockexchange.com/rns/0791G_-2018-2-27.pdf
 
 
 
 
 
HSBC Holdings plc
Report on Transition to IFRS 9 ‘Financial Instruments’
As at 1 January 2018
Issued 27 February 2018
 
 
 
 
 
 
 
 
The financial information on which this supplement is based is unaudited and has been prepared in accordance with the significant accounting policies of HSBC Holdings plc ('HSBC') as described in the Annual Report and Accounts 2017 and, for those policies impacted by HSBC’s adoption of IFRS 9 and IFRS 7 'Financial Instruments: Disclosures', within the appendix to this supplement. The financial information does not constitute financial statements prepared in accordance with International Financial Reporting Standards ('IFRSs'), is not complete and should be read in conjunction with the Annual Report and Accounts 2017 and other reports and financial information published by HSBC.
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
Contents
 
Page
Impact of IFRS 9
1
IAS 39/IAS 37 allowances to IFRS 9 ECL walk
1
Transition to IFRS 9 'Financial Instruments'
2
Credit risk profile
3
Measurement uncertainty and sensitivity analysis of ECL estimates
7
Credit quality of financial instruments
10
Impact on regulatory capital
14
Technical appendix – Transition disclosures required by accounting standards
16
Cautionary statement regarding forward-looking statements
26
On 1 January 2018, HSBC implemented the requirements of IFRS 9 ‘Financial Instruments’. This Report on Transition to IFRS 9 'Financial Instruments' provides information relevant to understanding the impact of the new accounting standard on HSBC’s financial position at 1 January 2018. The information supplements disclosures made in the Annual Report and Accounts 2017 and precedes those required in our 2018 financial statements. The transition disclosures provide a bridge between IAS 39 'Financial Instruments: Recognition and Measurement', IAS 37 'Provisions, Contingent Liabilities and Contingent Assets' and IFRS 9 results. They provide context for changes in the recognition of credit losses, changes in the classification and measurement of financial instruments on our balance sheet and the resulting impact on regulatory capital.
 
We continue to test and refine the new accounting processes, internal controls and governance framework necessitated by the adoption of IFRS 9. Therefore the estimation of expected credit losses (‘ECL’) and related impacts remains subject to change until finalisation of the financial statements for the year ending
31 December 2018.
 
 
Impact of IFRS 9
HSBC adopted the requirements of IFRS 9 ‘Financial Instruments’ on 1 January 2018, with the exception of the provisions relating to the presentation of gains and losses on financial liabilities designated at fair value, which were adopted on 1 January 2017. The impact of transitioning to IFRS 9 at 1 January 2018 on the consolidated financial statements of HSBC was a decrease in net assets of $1,004m, arising from:
 
 
a decrease of $2,232m from additional impairment allowances;
 
 
 
an increase of $908m from the remeasurement of financial assets and liabilities as a consequence of classification changes, mainly from revoking fair value accounting designations for certain long-dated issued debt instruments; and
 
 
 
an increase in net deferred tax assets of $320m.
HSBC remains strongly capitalised following the adoption of IFRS 9 which, based on the transition impact, will result in a 12bps increase in the common equity tier 1 ratio, applying the EU regulatory transitional arrangements, and a 1bp increase on a fully loaded basis at 1 January 2018.
 
 
IAS 39/IAS 37 allowances to IFRS 9 ECL walk
 
 
 
1
POCI - Purchased or originated credit impaired
Presented above is a high level walk of the IAS 39/IAS 37 credit-related allowances/provisions to the final IFRS 9 ECL allowance.
'ECL 12M' represents the increase in the allowance between IAS 39/IAS 37 and an IFRS 9 ECL associated with defaults in the next 12 months across all stages incorporating only the 'Central' scenario. The $1,280m increase is mainly a result of moving to an expected credit loss model from an incurred-loss model with loss emergence periods of generally less than 12 months.
 
 
 
'ECL lifetime' represents the incremental stage 2 ECL associated with defaults beyond 12 months under a lifetime expected credit loss estimation incorporating only the Central scenario ($804m).
'Multiple economic scenarios' represents the increase in ECL as a result of using multiple economic scenarios rather than a single Central scenario ($332m).
There was an immaterial change in allowances related to changes in classification and measurement and therefore this is not presented separately in the table above.
 
 
 
 
 
1
HSBC Holdings plc IFRS 9 2018

 
 
Transition to IFRS 9 'Financial Instruments'
Effect on business model
We do not expect the implementation of IFRS 9 to result in a significant change to HSBC's business model, or that of our four global businesses. This includes our strategy, country presence, product offerings and target customer segments.
Exposures in certain industry sectors, in particular those most sensitive to changes in economic conditions, will be affected to a greater degree under IFRS 9. However, we have established credit risk management processes in place and we actively assess the impact of economic developments in key markets on specific customers, customer segments or portfolios. If we foresee
 
changes in credit conditions, we will take mitigating action, including the revision of risk appetites or limits and tenors, as appropriate. In addition, we will continue to evaluate the terms under which we provide credit facilities within the context of individual customer requirements, the quality of the relationship, local regulatory requirements, market practices and our local market position.
Under IFRS 9, we will recognise expected credit losses on committed, undrawn exposures, including credit cards, loan commitments and financial guarantees. This will have the most significant impact on our credit card portfolio. We will continue to manage undrawn exposures and credit limits as part of our overall approach to capital management.
 
IFRS 9 process
The implementation of IFRS 9 represents a significant challenge to the risk and finance functions across the bank. IFRS 9 introduces new concepts and measures such as significant increase in credit risk and lifetime expected credit losses. Existing stress testing and regulatory models, skills and expertise were adapted in order to meet IFRS 9 requirements. Data from various client, financeand risk systems has been integrated and validated. As a result of IFRS 9 adoption, management has additional insight and measures not previously utilised which, over time, may influence our risk appetite and risk management processes. The IFRS 9 process comprises three main areas: modelling and data, implementation and governance.
Modelling
The risk function had pre-existing Basel and behavioural scorecards in most geographies. These models were enhanced or supplemented by additional models to deal with significant credit deterioration, lifetime expected credit losses and forward economic guidance as required by IFRS 9. The impairment models vary in complexity and inputs depending on the size of the portfolio, the amount of data available and the sophistication of the market concerned. The risk modelling function followed HSBC’s standard governance processes for developing new models as described in our Pillar 3 Disclosures at
31 December 2017 on page 20. Significant newly developed models have also been subject to independent review by our Independent Model Review function ('IMR').
IFRS 9 requires our measurement of ECL to consider forecasts of future economic conditions and to consider the possibility of more than one outcome. Our Group Risk Economics team has therefore developed new processes as described further on pages 7-10.
 
Implementation
A centralised impairment engine has been implemented to perform the ECL calculation in a globally consistent manner. The impairment calculation engine receives data from a variety of client, finance and risk systems. A number of data validation checks and enhancements are then performed prior to the ECL calculation taking place. Once the ECL calculation has been executed there are further data analysis checks and review and challenge of the results of the ECL calculation prior to commencing formal governance. As far as possible these checks and processes are performed in a globally consistent and centralised manner in order to achieve optimal effectiveness. Risk and Finance work closely together throughout the execution of this process.
Governance
A series of Regional Management Review Forums has been established in key sites/regions in order to review and approve the impairment results. Regional Management Review Forums have representatives from Credit Risk and Finance. The key site/ regional approvals are reported up to the Global Business Impairment Committee for final approval of the Group’s ECL for the period. The Global Heads of Wholesale Credit and Market Risk and Retail Banking and Wealth Management ('RBWM') Risk, the global business CFOs and the Group Chief Accounting Officer are required members of the committee.
 
 
 
 
 
 
HSBC Holdings plc IFRS 9 2018
2
 
 
 
Credit risk profile
The Group's total allowance for ECL is $10,201m. This comprises $9,480m in respect of assets held at amortised cost, $537m in respect of loan commitments and financial guarantees and $184m in respect of debt instruments measured at fair value through other comprehensive income ('FVOCI'). The following tables analyse the financial instruments to which the impairment requirements of IFRS 9 are applied and the related allowance for ECL.
 
 
 
 
 
 
 
Summary of financial instruments to which the impairment requirements in IFRS 9 are applied
 
Gross carrying/nominal amount
 
Allowance
for ECL1
 
 
$m
 
$m
 
Loans and advances to customers at amortised cost
959,080
 
(9,343
)
– personal
375,069
 
(3,047
)
– corporate and commercial
520,137
 
(6,053
)
– non-bank financial institutions
63,874
 
(243
)
Loans and advances to banks at amortised cost
82,582
 
(23
)
Other financial assets measured at amortised cost
557,864
 
(114
)
– cash and balances at central banks
180,624
 
(3
)
– items in the course of collection from other banks
6,628
 
 
– Hong Kong Government certificates of indebtedness
34,186
 
 
– reverse repurchase agreements – non-trading
201,553
 
 
– financial investments
59,539
 
(16
)
– prepayments, accrued income and other assets2
75,334
 
(95
)
Total gross carrying amount on balance sheet
1,599,526
 
(9,480
)
Loan and other credit related commitments
501,361
 
(376
)
– personal
196,093
 
(14
)
– corporate and commercial
262,391
 
(355
)
– financial
42,877
 
(7
)
Financial guarantees and similar contracts
89,382
 
(161
)
– personal
791
 
(4
)
– corporate and commercial
78,102
 
(153
)
– financial
10,489
 
(4
)
Total nominal amount off-balance sheet3
590,743
 
(537
)
At 1 Jan 2018
2,190,269
 
(10,017
)
 
 
 
 
Fair value
 
Memorandum allowance for
ECL4
 
 
$m
 
$m
 
At 1 Jan 2018
 
 
 
 
Debt instruments measured at fair value through other comprehensive income
322,163
 
(184
)
 
 
 
 
 
 
1
As explained further on page 19 of the Technical Appendix, the total ECL is recognised in the loss allowance for the financial asset unless the total ECL exceeds the gross carrying amount of the financial asset, in which case the ECL is recognised as a provision.
 
 
 
 
 
 
 
 
2
Includes only those financial instruments which are subject to the impairment requirements of IFRS 9. ‘Prepayments, accrued income and other assets’ as presented within the consolidated balance sheet on page 22 includes both financial and non-financial assets.
 
 
 
3
Represents the maximum amount at risk should the contracts be fully drawn upon and clients default.
 
 
 
4
For debt instruments measured at FVOCI, the allowance for ECL is a memorandum item. The debt instruments continue to be measured at fair value. The accounting for financial assets measured at FVOCI is explained further on page 17 of the Technical Appendix.
 
 
 
 
 
 
3
HSBC Holdings plc IFRS 9 2018
 
 
 
Summary of credit risk (excluding debt instruments measured at FVOCI) by stage distribution and ECL coverage by industry sector
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Gross carrying/nominal amount1
 
 
Allowance for ECL
 
 
ECL coverage %
 
 
Stage 1
 
Stage 2
 
Of which:
 
Of which:
 
Stage 3
 
POCI3
 
Total
 
 
Stage 1
 
Stage 2
 
Of which:
 
Of which:
 
Stage 3
 
POCI3
 
Total
 
 
Stage 1
Stage 2
Of which:
Of which:
Stage 3
POCI3
Total
 
 
 
1 to 29 DPD2
 
30 and > DPD2
 
 
 
 
 
 
 
1 to 29 DPD2
 
30 and > DPD2
 
 
 
 
 
 
 
1 to 29 DPD2
30 and > DPD2
 
 
 
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
 
%
%
%
%
%
%
%
Loans and advances to customers at amortised cost
871,566
 
72,658
 
2,393
 
2,447
 
13,882
 
974
 
959,080
 
 
(1,309
)
(2,201
)
(261
)
(261
)
(5,591
)
(242
)
(9,343
)
 
0.2
3.0
10.9
10.7
40.3
24.8
1.0
– personal
354,305
 
16,354
 
1,683
 
1,428
 
4,410
 
 
375,069
 
 
(581
)
(1,156
)
(218
)
(230
)
(1,310
)
 
(3,047
)
 
0.2
7.1
13.0
16.1
29.7
0.8
– corporate and commercial
456,837
 
53,262
 
684
 
977
 
9,064
 
974
 
520,137
 
 
(701
)
(1,037
)
(42
)
(31
)
(4,073
)
(242
)
(6,053
)
 
0.2
1.9
6.1
3.2
44.9
24.8
1.2
– non-bank financial institutions
60,424
 
3,042
 
26
 
42
 
408
 
 
63,874
 
 
(27
)
(8
)
(1
)
 
(208
)
 
(243
)
 
0.3
3.8
51.0
0.4
Loans and advances to banks at amortised cost
81,027
 
1,540
 
7
 
66
 
15
 
 
82,582
 
 
(17
)
(4
)
(2
)
 
(2
)
 
(23
)
 
0.3
28.6
13.3
Other financial assets measured at amortised cost
556,185
 
1,517
 
133
 
46
 
155
 
7
 
557,864
 
 
(28
)
(4
)
 
(1
)
(82
)
 
(114
)
 
0.3
2.2
52.9
Loan and other credit related commitments
475,986
 
24,330
 
 
 
999
 
46
 
501,361
 
 
(126
)
(183
)
 
 
(67
)
 
(376
)
 
0.8
 
 
6.7
0.1
– personal
194,320
 
1,314
 
 
 
459
 
 
196,093
 
 
(13
)
(1
)
 
 
 
 
(14
)
 
0.1
 
 
– corporate and commercial
240,854
 
20,951
 
 
 
540
 
46
 
262,391
 
 
(108
)
(180
)
 
 
(67
)
 
(355
)
 
0.9
 
 
12.4
0.1
– financial
40,812
 
2,065
 
 
 
 
 
42,877
 
 
(5
)
(2
)
 
 
 
 
(7
)
 
0.1
 
 
Financial guarantee and similar contracts
77,921
 
11,014
 
 
 
413
 
34
 
89,382
 
 
(36
)
(47
)
 
 
(78
)
 
(161
)
 
0.4
 
 
18.9
0.2
– personal
768
 
18
 
 
 
5
 
 
791
 
 
 
(2
)
 
 
(2
)
 
(4
)
 
11.1
 
 
40.0
0.5
– corporate and commercial
67,596
 
10,064
 
 
 
408
 
34
 
78,102
 
 
(35
)
(44
)
 
 
(74
)
 
(153
)
 
0.1
0.4
 
 
18.1
0.2
– financial
9,557
 
932
 
 
 
 
 
10,489
 
 
(1
)
(1
)
 
 
(2
)
 
(4
)
 
0.1
 
 
At 1 Jan 2018
2,062,685
 
111,059
 
 
 
15,464
 
1,061
 
2,190,269
 
 
(1,516
)
(2,439
)
 
 
(5,820
)
(242
)
(10,017
)
 
0.1
2.2
 
 
37.6
22.8
0.5
1         
Represents the maximum amount at risk should the contracts be fully drawn upon and clients default.                                                                                                  
 
 
 
2
Days past due ('DPD'). Up to date accounts in Stage 2 are not shown in amounts presented above.
 
 
 
3
Purchased or originated credit-impaired ('POCI').
 
 
 
 
 
 
HSBC Holdings plc IFRS 9 2018
4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Personal lending – geographical summary of loans and advances at amortised cost by stage distribution and ECL coverage
 
 
Gross carrying amount
Allowance for ECL
 
 
Stage 1
 
Stage 2
 
Stage 3
 
Total
 
Stage 1
 
Stage 2
 
Stage 3
 
Total
 
ECL coverage
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
%
First lien residential mortgages
266,879
 
8,299
 
2,921
 
278,099
 
(60
)
(67
)
(533
)
(660
)
0.2
Europe
123,925
 
1,647
 
1,203
 
126,775
 
(14
)
(34
)
(272
)
(320
)
0.3
– of which: UK
117,725
 
1,170
 
876
 
119,771
 
(8
)
(22
)
(155
)
(185
)
0.2
Asia
106,926
 
2,289
 
247
 
109,462
 
(36
)
(11
)
(26
)
(73
)
0.1
– of which: Hong Kong
69,460
 
748
 
36
 
70,244
 
 
 
(3
)
(3
)
MENA
2,081
 
79
 
214
 
2,374
 
(2
)
(2
)
(117
)
(121
)
5.1
North America
32,021
 
4,191
 
1,118
 
37,330
 
(4
)
(13
)
(109
)
(126
)
0.3
Latin America
1,926
 
93
 
139
 
2,158
 
(4
)
(7
)
(9
)
(20
)
0.9
Credit cards
22,576
 
2,797
 
422
 
25,795
 
(298
)
(663
)
(273
)
(1,234
)
4.8
Europe
9,470
 
643
 
89
 
10,202
 
(84
)
(124
)
(42
)
(250
)
2.5
– of which: UK
9,051
 
617
 
87
 
9,755
 
(82
)
(120
)
(39
)
(241
)
2.5
Asia
9,871
 
1,420
 
99
 
11,390
 
(121
)
(253
)
(58
)
(432
)
3.8
– of which: Hong Kong
6,707
 
1,121
 
18
 
7,846
 
(37
)
(187
)
(16
)
(240
)
3.1
MENA
1,239
 
152
 
140
 
1,531
 
(41
)
(86
)
(103
)
(230
)
15.0
North America
816
 
206
 
15
 
1,037
 
(9
)
(49
)
(11
)
(69
)
6.7
Latin America
1,180
 
376
 
79
 
1,635
 
(43
)
(151
)
(59
)
(253
)
15.5
Other personal lending
64,850
 
5,258
 
1,067
 
71,175
 
(223
)
(426
)
(504
)
(1,153
)
1.6
Europe
29,501
 
2,234
 
453
 
32,188
 
(79
)
(108
)
(188
)
(375
)
1.2
– of which: UK
8,459
 
1,440
 
151
 
10,050
 
(74
)
(92
)
(66
)
(232
)
2.3
Asia
27,281
 
1,411
 
312
 
29,004
 
(43
)
(102
)
(108
)
(253
)
0.9
– of which: Hong Kong
18,601
 
772
 
127
 
19,500
 
(34
)
(62
)
(29
)
(125
)
0.6
MENA
2,607
 
248
 
111
 
2,966
 
(21
)
(35
)
(93
)
(149
)
5.0
North America
3,582
 
469
 
102
 
4,153
 
(16
)
(35
)
(27
)
(78
)
1.9
Latin America
1,879
 
896
 
89
 
2,864
 
(64
)
(146
)
(88
)
(298
)
10.4
At 1 Jan 2018
354,305
 
16,354
 
4,410
 
375,069
 
(581
)
(1,156
)
(1,310
)
(3,047
)
0.8
Stage distribution is fairly consistent across First Lien Mortgages, Credit Cards, and Other Personal Lending with a higher proportion in Stage 1 in Asia and Europe than the other regions. The ECL coverage is lower in mortgages relative to credit cards and other personal lending, driven by the collateralised nature of the mortgage portfolio. The higher ECL coverage in MENA mortgages is due to the significant levels of ECL on defaulted mortgages. The higher ECL coverage on credit cards and other personal lending in Latin America and MENA is due to relative differences in credit outcomes as compared to the other regions.
 
 
 
 
 
 
5
HSBC Holdings plc IFRS 9 2018
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Wholesale lending – geographical summary of loans and advances at amortised cost by stage distribution and ECL coverage
 
 
Gross carrying amount
Allowance for ECL
 
 
Stage 1
 
Stage 2
 
Stage 3
 
POCI
 
Total
 
Stage 1
 
Stage 2
 
Stage 3
 
POCI
 
Total
 
ECL coverage
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
%
Corporate and Commercial
456,837
 
53,262
 
9,064
 
974
 
520,137
 
(701
)
(1,037
)
(4,073
)
(242
)
(6,053
)
1.2
Europe
161,907
 
14,455
 
4,925
 
558
 
181,845
 
(359
)
(497
)
(1,869
)
(99
)
(2,824
)
1.6
– of which: UK
114,999
 
10,340
 
3,377
 
297
 
129,013
 
(298
)
(435
)
(1,197
)
(19
)
(1,949
)
1.5
Asia
224,858
 
23,040
 
1,480
 
158
 
249,536
 
(181
)
(158
)
(967
)
(24
)
(1,330
)
0.5
– of which: Hong Kong
139,554
 
14,636
 
590
 
124
 
154,904
 
(89
)
(90
)
(399
)
(22
)
(600
)
0.4
MENA
15,035
 
4,910
 
1,361
 
218
 
21,524
 
(47
)
(105
)
(856
)
(115
)
(1,123
)
5.2
North America
43,993
 
9,756
 
1,018
 
 
54,767
 
(24
)
(255
)
(251
)
 
(530
)
1.0
Latin America
11,044
 
1,101
 
280
 
40
 
12,465
 
(90
)
(22
)
(130
)
(4
)
(246
)
2.0
Non-bank financial institutions
60,424
 
3,042
 
408
 
 
63,874
 
(27
)
(8
)
(208
)
 
(243
)
0.4
Europe
28,063
 
932
 
305
 
 
29,300
 
(7
)
(3
)
(145
)
 
(155
)
0.5
– of which: UK
24,007
 
828
 
230
 
 
25,065
 
(4
)
(3
)
(140
)
 
(147
)
0.6
Asia
22,578
 
759
 
26
 
 
23,363
 
(6
)
(3
)
(18
)
 
(27
)
0.1
– of which: Hong Kong
11,874
 
602
 
26
 
 
12,502
 
(3
)
(1
)
(18
)
 
(22
)
0.2
MENA
1,038
 
1
 
68
 
 
1,107
 
(10
)
(1
)
(39
)
 
(50
)
4.5
North America
7,609
 
1,346
 
9
 
 
8,964
 
(1
)
(1
)
(6
)
 
(8
)
0.1
Latin America
1,136
 
4
 
 
 
1,140
 
(3
)
 
 
 
(3
)
0.3
Banks
81,027
 
1,540
 
15
 
 
82,582
 
(17
)
(4
)
(2
)
 
(23
)
Europe
12,886
 
342
 
15
 
 
13,243
 
(5
)
(2
)
(2
)
 
(9
)
0.1
– of which: UK
4,563
 
261
 
 
 
4,824
 
(3
)
(1
)
 
 
(4
)
0.1
Asia
49,598
 
475
 
 
 
50,073
 
(6
)
(1
)
 
 
(7
)
– of which: Hong Kong
20,318
 
132
 
 
 
20,450
 
(4
)
 
 
 
(4
)
MENA
6,402
 
72
 
 
 
6,474
 
(1
)
(1
)
 
 
(2
)
North America
8,690
 
642
 
 
 
9,332
 
(1
)
 
 
 
(1
)
Latin America
3,451
 
9
 
 
 
3,460
 
(4
)
 
 
 
(4
)
0.1
At 1 Jan 2018
598,288
 
57,844
 
9,487
 
974
 
666,593
 
(745
)
(1,049
)
(4,283
)
(242
)
(6,319
)
0.9
Stage distribution is fairly consistent across the regions except MENA and North America where certain obligors have significantly deteriorated in credit risk. The higher ECL coverage in the MENA corporate and commercial industry sector is driven by long-dated exposures in the oil and gas sector. In Asia the ECL coverage is lower due to the shorter average contractual tenor in this region particularly in China and Hong Kong.
The Group’s defaulted and credit deteriorated exposures are concentrated in the UK, Hong Kong, US and MENA, typically relating to the oil and gas and commercial real estate sectors.
 
 
 
 
 
 
HSBC Holdings plc IFRS 9 2018
6
 
 
 
Measurement uncertainty and sensitivity
analysis of ECL estimates
The recognition and measurement of ECL is highly complex and involves the use of significant judgement and estimation, including in the formulation and incorporation of multiple forward-looking economic conditions into the ECL estimates to meet the measurement objective of IFRS 9.
Methodology
HSBC has adopted the use of three economic scenarios in most economic environments. These scenarios are representative of HSBC's view of forecast economic conditions, sufficient to calculate unbiased ECL. They represent a 'most likely outcome', (the Central scenario) and two, less likely, 'Outer' scenarios on either side of the Central, referred to as an 'Upside' and a 'Downside' scenario respectively. Each Outer scenario is consistent with a probability of 10% while the Central scenario is assigned the remaining 80%. This weighting scheme is deemed as being appropriate for the computation of unbiased ECL. Key scenario assumptions are set using the average of forecasts from external economists. This helps ensure that the IFRS 9 scenarios are unbiased and maximise the use of independent information.
For the Central scenario, HSBC sets key assumptions such as GDP growth, inflation, unemployment and policy rates using either the average of external forecasts (commonly referred to as consensus forecasts) for most economies or market prices. An external vendor’s global macro model, which is conditioned to follow the consensus forecasts, projects the other paths required as inputs to credit models. This vendor model is subject to HSBC’s risk governance framework with oversight by a specialist internal unit.
Upside and Downside scenarios are designed to be cyclical in that GDP growth, inflation and unemployment usually revert back to the Central scenario after the first three years for major economies. We determine the maximum divergence of GDP growth from the Central scenario using the 10th and the 90th percentile of the entire distribution of forecast outcomes for major economies. Using externally available forecast distributions ensures independence in scenario construction. While key economic variables are set with reference to external distributional forecasts, we also align the overall narrative of the scenarios to the macroeconomic risks described in HSBC's top and emerging risks. This ensures that scenarios remain consistent with the more qualitative assessment of risks captured in top and emerging risks. We project additional variable paths using the external vendor’s global macro model.
The Central, Upside and Downside scenarios selected with reference to external forecast distributions using the above approach are termed the ‘Consensus Economic Scenarios’.
We apply the following to generate the three economic scenarios:
 
 
Economic risk assessment – We develop a shortlist of the downside and upside economic and political risks most relevant to HSBC and the IFRS 9 measurement objective. These risks include local and global economic/political risks that together impact on economies that materially matter to HSBC, namely UK, euro area, Hong Kong, China and US. We compile this list by monitoring developments in the global economy, assessing the risks identified in HSBC's top and emerging risks, and through external and internal consultations with subject matter experts.
 
 
 
 
Scenario generation – For the Central scenario, we obtain a pre-defined set of economic forecasts from the average forecast taken from the consensus forecast survey of professional forecasters. Paths for the Outer scenarios are benchmarked to the Central scenario and reflect the economic risk assessment. Scenario probabilities reflect management judgement and are informed by data analysis ofpast recessions (transitions in and out of recession) and the current economic outlook. For any scenario, the key assumptions made and the accompanying paths represent our 'best estimate' of a scenario at a specified probability. Suitable narratives are developed for the Central scenario and the paths of the Outer scenarios.
 
 
 
Variable enrichment – We expand each scenario through enrichment of variables. This includes the production of 400+ variables that are required by the businesses. The external vendor expands these scenarios by using as inputs the agreed scenario narratives and the variables aligned to these narratives. Scenarios, once expanded, continue to be benchmarked to the latest events and information. Late breaking events could lead to revision of scenarios to reflect management judgement.
HSBC recognises that the Consensus Economic Scenario approach using three scenarios will be insufficient in certain economic environments. Additional analysis may be requested at management’s discretion, including the production of extra scenarios. While we anticipate that there will be only limited instances when the standard approach will not apply, we have occasion to invoke this additional step at 1 January 2018, due to the specific uncertainties facing the UK economy at this time, resulting in the recognition of additional ECL, 'a management overlay' for economic uncertainty.
Description of Consensus Economic Scenarios
The Central scenario
HSBC’s Central scenario is one of steady growth over the forecast period 2018–2022. Global GDP growth is expected to be 2.9% on average over the period, which is marginally higher than the average growth rate over the period 2011–2016. Across the key markets, we note that:
 
 
Expected average rates of growth over the 2018–2022 period are lower than those experienced in the recent past for the UK, China, Hong Kong, Canada and the UAE. For the UK, this forecast reflects current views on the UK's exit from the EU, while for China, this suggests rebalancing at a pace in line with expectations.
 
 
 
French GDP forecasts are stronger for the forecast period compared with recent history. Supportive factors include the recent cyclical upswing, longer-term expectations of reform and euro-area recovery.
Core inflation has remained stable and inflation in the US and euro area is expected to only slowly converge back towards central bank targets over the next two years. As a consequence, US and euro area central banks are expected to raise rates very gradually. In the UK, the Bank of England is expected to look through near-term, above-target inflation and raise interest rates slowly.
Unemployment rates displayed considerable positive cyclical momentum in 2017 across our key markets and such momentum is expected to continue to underpin labour market performance in the forecast period. Central scenario forecasts of the unemployment rate are stable and, for some markets, at historical lows.
Stabilisation of oil prices in 2017, helped by the Organization of Petroleum Exporting Countries' output cuts and a fall in inventory, has enabled a stronger price outlook to develop. Despite this, Central scenario oil price forecasts are moderate with the price reaching $68 per barrel by the end of the forecast period.
 
 
 
 
 
7
HSBC Holdings plc IFRS 9 2018
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Central scenario (average 2018–2022)
 
UK
 
France
 
Hong
Kong
 
Mainland
China
 
UAE
 
US
 
Canada
 
Mexico
 
GDP growth rate (%)
1.8
 
1.5
 
2.4
 
5.8
 
3.5
 
2.1
 
1.8
 
2.7
 
Inflation (%)
2.2
 
N/A
 
2.5
 
2.3
 
2.9
 
2.1
 
2.0
 
3.5
 
Unemployment (%)
5.2
 
8.6
 
3.4
 
4.0
 
N/A
 
4.6
 
6.3
 
4.0
 
House price growth (%)
2.8
 
3.9
 
3.6
 
5.4
 
6.2
 
3.6
 
3.1
 
6.2
 
Note: N/A - not required in credit models
The Upside scenario
Global real GDP growth rises in the first two years of the Upside before converging to the Central scenario. Improved confidence, accommodative monetary policy, fiscal expansion across major
 
economies, including tax reform in the US and diminished political risk are the key risk themes that support the year-end Upside scenario.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Upside scenario (average 2018–2022)
 
UK
 
France
 
Hong
Kong
 
Mainland
China
 
UAE
 
US
 
Canada
 
Mexico
 
GDP growth rate (%)
2.5
 
1.9
 
2.8
 
6.0
 
4.0
 
2.7
 
2.2
 
3.2
 
Inflation (%)
2.5
 
N/A
 
2.9
 
2.7
 
3.3
 
2.4
 
2.2
 
3.9
 
Unemployment (%)
4.8
 
8.3
 
3.2
 
3.7
 
N/A
 
4.1
 
6.1
 
3.6
 
House price growth (%)
4.0
 
4.6
 
4.0
 
6.9
 
7.7
 
4.9
 
4.3
 
6.8
 
Note: N/A - not required in credit models
The Downside scenario
Globally, real GDP growth declines for two years in the Downside scenario before recovering to the Central scenario. House price growth either stalls or contracts and equity markets correct abruptly. The global slowdown in demand drives commodity
 
prices lower and inflation falls. Central banks remain accommodative. This is consistent with the risk themes of rising protectionism, central bank policy uncertainty, mainland China choosing to rebalance at a faster pace and an absence of fiscal support.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Downside scenario (average 2018–2022)
 
UK
 
France
 
Hong
Kong
 
Mainland
China
 
UAE
 
US
 
Canada
 
Mexico
 
GDP growth rate (%)
1.2
 
1.1
 
2.0
 
5.5
 
3.0
 
1.3
 
1.6
 
2.1
 
Inflation (%)
1.8
 
N/A
 
2.2
 
2.0
 
2.6
 
1.8
 
1.9
 
3.1
 
Unemployment (%)
5.6
 
9.0
 
3.8
 
4.2
 
N/A
 
5.1
 
6.7
 
4.5
 
House price growth (%)
0.9
 
0.8
 
1.7
 
3.0
 
4.5
 
1.1
 
0.6
 
5.4
 
Note: N/A - not required in credit models
The following graphs show the historical and forecasted GDP growth for the three economic scenarios for the four largest economies where HSBC has operations.
 
 
US
 
 
 
 
UK
 
 
 
 
 
 
HSBC Holdings plc IFRS 9 2018
8
 
 
 
Hong Kong
 
 
Mainland China
How economic scenarios are reflected in the wholesale calculation of ECL
HSBC has developed a globally consistent methodology for the application of forward economic guidance ('FEG') into the calculation of ECL by incorporating FEG into the estimation of the term structure of probability of default ('PD') and loss given default ('LGD'). For PDs, we consider the correlation of FEG to default rates for a particular industry in a country. For LGD calculations we consider the correlation of FEG 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 stage 3 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, HSBC incorporates FEG via the application of a scalar. The scalar reflects the ratio of the probability-weighted outcome to the Central scenario outcome for non-stage 3 populations.
How economic scenarios are reflected in the retail calculation of ECL
The impact of FEG on PD is modelled at a portfolio level. Historic relationships between observed default rates and macroeconomic variables are integrated into IFRS 9 ECL estimates by leveraging economic response models. The impact of FEG on PD is modelled over a period equal to the remaining maturity of underlying asset(s). 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 leveraging national level forecasts of the house price index ('HPI') and applying the corresponding LGD expectation.
Impact of multiple economic scenarios on ECL
The ECL recognised in the financial statements (the ‘IFRS 9 ECL’) reflects the effect on expected credit losses of a range of possible outcomes, calculated on a probability-weighted basis, based on the economic scenarios described above, including management overlays where required. The probability-weighted amount is typically a higher number than would result from using only the Central (most likely) economic scenario. Expected losses typically have a non-linear relationship to the many factors which influence credit losses such that more favourable macroeconomic factors do not reduce defaults as much as less favourable macroeconomic factors increase defaults. The tables below compares IFRS 9 ECL and the ECL number prepared using only Central Scenario assumptions. A higher number indicates a morenon-linear relationship between these factors and credit losses across the range of possible outcomes considered, and therefore a greater degree of uncertainty in loss outcome. The amount of this difference is approximately 3% of ECL across the Group reflecting the relatively stable and benign economic outlook across most markets. Larger differences are shown in the below table.
 
 
 
 
 
 
 
 
 
 
IFRS 9 ECL as compared to Central scenario ECL
Country of booking
Central scenario ECL
 
IFRS 9 ECL
 
Difference
 
 
$m
 
$m
 
$m
 
UK
2,751
 
3,068
 
317
 
Mexico
761
 
779
 
18
 
US
587
 
590
 
3
 
Hong Kong
1,050
 
1,035
 
(15
)
Other
4,720
 
4,729
 
9
 
Total
9,869
 
10,201
 
332
 
UK economic uncertainty
A management overlay of $245m has been included in the IFRS 9 ECL numbers in the table above, adding to the result from the consensus economic scenarios, of which $150m relates to wholesale and $95m to retail, to address the current economic uncertainty in the UK. The overlay reflects management’s judgement that the consensus economic scenarios do not fully reflect the high degree of uncertainty in estimating the distribution of ECL for UK portfolios under these conditions. In arriving at the overlay, the following risks were considered and alternative scenarios modelled to understand potential impacts:
 
 
Alternative scenario (a): While the Central scenario reflects current consensus forecasts, there is the potential for large forecast revisions in the coming quarters, as economic and political events unfold. The consensus Downside scenario was modelled as an alternative to the consensus Central scenario to understand the impact of a significant downward shift in consensus forecasts.
 
 
 
Alternative scenario (b): Management modelled a further downside scenario of similar severity to but longer duration than the consensus Downside scenario, to reflect the risk that in a downside scenario there may be a longer term impact on growth than that currently envisaged.
 
 
 
Alternative scenario (c): Finally, management modelled an alternative severe downside scenario reflecting a deeper cyclical shock resulting in a steep depreciation in sterling and an increase in inflation with an associated monetary policy response.
The table below compares the core macroeconomic variables under the consensus Central and Upside scenarios, shown as averages 2018–2022, to the most severe assumptions relating to the consensus and alternative scenarios:
 
 
 
 
 
9
HSBC Holdings plc IFRS 9 2018
 
 
 
 
 
 
 
 
 
UK
 
GDP growth %
 
Unemployment level %
 
Consensus upside (5 year average)
2.5
 
4.8
 
Consensus central (5 year average)
1.8
 
5.2
 
Consensus downside (central under Alternative (a)) (most severe value)
0.1
 
6.3
 
Alternative (b) (most severe value)
(1.0
)
7.2
 
Alternative (c) (most severe value)
(2.4
)
8.9
 
 
The overlay adjusts the ECL calculated on the UK consensus economic scenarios to reflect the alternative scenarios described above, within the 10:80:10 weighting scheme, as follows: half the impact of Alternative scenario (a) is included, in effect giving equal weighting within the central band to consensus Central and consensus Downside assumptions. For the downside, the overlay has the effect of replacing the consensus Downside with Alternative scenario (b) but including a small risk of Alternative scenario (c).
The management overlay for UK economic uncertainty will be reviewed regularly in the light of new information about the macroeconomic outlook and leading credit risk indicators, and adjusted as necessary to reflect movements in the consensus economic assumptions and the degree of uncertainty with which they are associated.
 
 
 
 
 
 
Credit quality of financial instruments
The following tables summarise the credit quality of the financial instruments that are subjected to IFRS 9 impairment requirement by stages. The credit quality disclosed in the tables is point-in-time as at 1 January 2018. It is not directly comparable to the significant increase in credit risk of the financial instruments as this is determined based on the relative increase in credit risk since initial recognition.
 
 
 
 
 
HSBC Holdings plc IFRS 9 2018
10
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Distribution of financial instruments to which the impairment requirements in IFRS 9 are applied, by credit quality and stage allocation
 
Gross carrying/notional amount
 
 
 
Strong
 
Good
 
Satisfactory
 
Sub-standard
 
Credit- impaired
 
Total
 
Allowance for ECL
 
 Net
 
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
Loans and advances to customers at amortised cost
479,067
 
227,146
 
220,089
 
17,922
 
14,856
 
959,080
 
(9,343
)
949,737
 
– stage 1
475,881
 
211,084
 
180,002
 
4,599
 
 
871,566
 
(1,309
)
870,257
 
– stage 2
3,186
 
16,062
 
40,087
 
13,323
 
 
72,658
 
(2,201
)
70,457
 
– stage 3
 
 
 
 
13,882
 
13,882
 
(5,591
)
8,291
 
– POCI
 
 
 
 
974
 
974
 
(242
)
732
 
Loans and advances to banks at amortised cost
70,959
 
7,692
 
3,890
 
26
 
15
 
82,582
 
(23
)
82,559
 
– stage 1
70,024
 
7,351
 
3,642
 
10
 
 
81,027
 
(17
)
81,010
 
– stage 2
935
 
341
 
248
 
16
 
 
1,540
 
(4
)
1,536
 
– stage 3
 
 
 
 
15
 
15
 
(2
)
13
 
– POCI
 
 
 
 
 
 
 
 
Other financial assets measured at amortised cost
469,898
 
47,347
 
39,595
 
862
 
162
 
557,864
 
(114
)
557,750
 
– stage 1
469,691
 
47,019
 
38,929
 
546
 
 
556,185
 
(28
)
556,157
 
– stage 2
207
 
328
 
666
 
316
 
 
1,517
 
(4
)
1,513
 
– stage 3
 
 
 
 
155
 
155
 
(82
)
73
 
– POCI
 
 
 
 
7
 
7
 
 
7
 
Loan and other credit-related commitments
297,683
 
121,508
 
74,694
 
6,431
 
1,045
 
501,361
 
(376
)
500,985
 
– stage 1
294,958
 
115,008
 
64,429
 
1,591
 
 
475,986
 
(126
)
475,860
 
– stage 2
2,725
 
6,500
 
10,265
 
4,840
 
 
24,330
 
(183
)
24,147
 
– stage 3
 
 
 
 
999
 
999
 
(67
)
932
 
– POCI
 
 
 
 
46
 
46
 
 
46
 
Financial guarantees and similar contracts
35,537
 
27,084
 
23,366
 
2,948
 
447
 
89,382
 
(161
)
89,221
 
– stage 1
33,558
 
25,009
 
18,095
 
1,259
 
 
77,921
 
(36
)
77,885
 
– stage 2
1,979
 
2,075
 
5,271
 
1,689
 
 
11,014
 
(47
)
10,967
 
– stage 3
 
 
 
 
413
 
413
 
(78
)
335
 
– POCI
 
 
 
 
34
 
34
 
 
34
 
At 1 Jan 2018
1,353,144
 
430,777
 
361,634
 
28,189
 
16,525
 
2,190,269
 
(10,017
)
2,180,252
 
 
 
 
 
 
 
 
 
 
Debt instruments at FVOCI1
 
 
 
 
 
 
 
 
– stage 1
297,753
 
6,678
 
12,941
 
2,450
 
 
319,822
 
(28
)
319,794
 
– stage 2
208
 
108
 
147
 
1,826
 
 
2,289
 
(142
)
2,147
 
– stage 3
 
 
 
 
584
 
584
 
(14
)
570
 
– POCI
 
 
 
 
 
 
 
 
At 1 Jan 2018
297,961
 
6,786
 
13,088
 
4,276
 
584
 
322,695
 
(184
)
322,511
 
 
 
 
 
 
 
 
 
 
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.
 
 
 
 
 
 
 
 
 
 
 
Quality classification definitions
‘Strong’ exposures demonstrate a strong capacity to meet financial commitments, with negligible or low probability of default.
‘Good’ exposures demonstrate a good capacity to meet financial commitments, with low default risk.
‘Satisfactory’ exposures require closer monitoring and demonstrate an average to fair capacity to meet financial commitments, with moderate default risk.
‘Sub-standard’ exposures require varying degrees of special attention and default risk is of greater concern.
‘Credit-impaired’ exposures have been assessed as impaired.
The five credit quality classifications defined above each encompass a range of granular internal credit rating grades assigned to wholesale and retail lending businesses and the external ratings attributed by external agencies to debt securities, as shown in the table below. Under IAS 39 retail lending credit quality was disclosed based on expected-loss percentages. Under IFRS 9 retail lending credit quality is now disclosed based on a twelve-month probability-weighted PD. The credit quality classifications for wholesale lending are unchanged and are based on internal credit risk ratings.
 
 
 
 
 
 
 
 
 
 
 
 
Credit quality classification
 
 
Debt securities
and other bills
Wholesale lending
Retail lending
 
 
External
credit rating
Internal
credit rating
12-month Basel probability of
default %
 
Internal
credit rating
12 month probability- weighted PD %
 
Quality classification
 
 
 
 
 
 
Strong
 
A- and above
CRR1 to CRR2
0.000 - 0.169
 
Band 1 and 2
0.000 - 0.500
 
Good
 
BBB+ to BBB-
CRR3
0.170 - 0.740
 
Band 3
0.501 - 1.500
 
Satisfactory
 
BB+ to B and unrated
CRR4 to CRR5
0.741 - 4.914
 
Band 4 and 5
1.501 - 20.000
 
Sub-standard
 
B- to C
CRR6 to CRR8
4.915 - 99.999
 
Band 6
20.001 - 99.999
 
Credit-impaired
 
Default
CRR9 to CRR10
100.000
 
Band 7
100.000
 
 
 
 
 
 
11
HSBC Holdings plc IFRS 9 2018
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Personal lending – credit risk profile by internal PD band for loans and advances at amortised cost
 
 
Gross carrying amount
Allowance for ECL
 
 
PD range1
Stage 1
 
Stage 2
 
Stage 3
 
Total
 
Stage 1
 
Stage 2
 
Stage 3
 
Total
 
ECL coverage
 
%
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
%
First lien residential mortgages
 
266,879
 
8,299
 
2,921
 
278,099
 
(60
)
(67
)
(533
)
(660
)
0.2
Band 1
0.000 to 0.250
235,249
 
339
 
 
235,588
 
(43
)
(1
)
 
(44
)
Band 2
0.251 to 0.500
17,350
 
535
 
 
17,885
 
(3
)
(2
)
 
(5
)
Band 3
0.501 to 1.500
9,316
 
3,975
 
 
13,291
 
(7
)
(6
)
 
(13
)
0.1
Band 4
1.501 to 5.000
3,524
 
1,236
 
 
4,760
 
(6
)
(8
)
 
(14
)
0.3
Band 5
5.001 to 20.000
1,414
 
1,177
 
 
2,591
 
(1
)
(21
)
 
(22
)
0.8
Band 6
20.001 to 99.999
26
 
1,037
 
 
1,063
 
 
(29
)
 
(29
)
2.7
Band 7
100.000
 
 
2,921
 
2,921
 
 
 
(533
)
(533
)
18.2
Other personal lending
 
87,426
 
8,055
 
1,489
 
96,970
 
(521
)
(1,089
)
(777
)
(2,387
)
2.5
Band 1
0.000 to 0.250
41,026
 
369
 
 
41,395
 
(73
)
 
 
(73
)
0.2
Band 2
0.251 to 0.500
9,761
 
342
 
 
10,103
 
(48
)
 
 
(48
)
0.5
Band 3
0.501 to 1.500
20,971
 
657
 
 
21,628
 
(117
)
(1
)
 
(118
)
0.5
Band 4
1.501 to 5.000
12,930
 
2,091
 
 
15,021
 
(172
)
(157
)
 
(329
)
2.2
Band 5
5.001 to 20.000
2,719
 
3,403
 
 
6,122
 
(111
)
(469
)
 
(580
)
9.5
Band 6
20.001 to 99.999
19
 
1,193
 
 
1,212
 
 
(462
)
 
(462
)
38.1
Band 7
100.000
 
 
1,489
 
1,489
 
 
 
(777
)
(777
)
52.2
At 1 Jan 2018
 
354,305
 
16,354
 
4,410
 
375,069
 
(581
)
(1,156
)
(1,310
)
(3,047
)
0.8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
12 month point-in-time (PiT) PD adjusted for multiple economic scenarios.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
HSBC Holdings plc IFRS 9 2018
12
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Wholesale lending – credit risk profile by obligor grade for loans and advances at amortised cost
 
Basel one-year PD range
Gross carrying amount
Allowance for ECL
ECL coverage
Mapped external rating
 
Stage 1
 
Stage 2
 
Stage 3
 
POCI
 
Total
 
Stage 1
 
Stage 2
 
Stage 3
 
POCI
 
Total
 
 
%
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
$m
 
%
 
Corporate & commercial
 
456,837
 
53,262
 
9,064
 
974
 
520,137
 
(701
)
(1,037
)
(4,073
)
(242
)
(6,053
)
1.2
 
CRR 1
0.000 to 0.053
43,578
 
440
 
 
 
44,018
 
(7
)
(3
)
 
 
(10
)
AA- and above
CRR 2
0.054 to 0.169
96,876
 
1,016
 
 
 
97,892
 
(25
)
(1
)
 
 
(26
)
A+ to A-
CRR 3
0.170 to 0.740
163,453
 
10,373
 
 
 
173,826
 
(173
)
(86
)
 
 
(259
)
0.1
BBB+ to BBB-
CRR 4
0.741 to 1.927
107,755
 
16,368
 
 
20
 
124,143
 
(256
)
(232
)
 
 
(488
)
0.4
BB+ to BB-
CRR 5
1.928 to 4.914
41,042
 
14,337
 
 
 
55,379
 
(190
)
(192
)
 
 
(382
)
0.7
BB- to B
CRR 6
4.915 to 8.860
2,641
 
6,363
 
 
27
 
9,031
 
(35
)
(272
)
 
(1
)
(308
)
3.4
B-
CRR 7
8.861 to 15.000
881
 
2,528
 
 
 
3,409
 
(6
)
(107
)
 
 
(113
)
3.3
CCC+
CRR 8
15.001 to 99.999
611
 
1,837
 
 
 
2,448
 
(9
)
(144
)
 
 
(153
)
6.3
CCC to C
CRR 9/10
100.000
 
 
9,064
 
927
 
9,991
 
 
 
(4,073
)
(241
)
(4,314
)
43.2
D
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Non-bank financial institutions
 
60,424
 
3,042
 
408
 
 
63,874
 
(27
)
(8
)
(208
)
 
(243
)
0.4
 
CRR 1
0.000 to 0.053
14,210
 
1
 
 
 
14,211
 
(1
)
 
 
 
(1
)
AA- and above
CRR 2
0.054 to 0.169
17,831
 
144
 
 
 
17,975
 
(2
)