ifrs 9: an analysis of q2 impacts and expectations ahead · 2020. 9. 15. · uk b1 uk b2 uk b3 uk...

IFRS 9: an analysis of Q2impacts and expectationsahead

15 September 2020

Agenda

IFRS 9: an analysis of Q2 impacts and expectations ahead2

IFRS 9: EY analysis of Q2 impacts and expectations ahead

Tara Kengla,IFRS 9 Solution Leader

Ernst & Young LLP

Introduction and Q&A

Laure Guégan,European FinancialServices IFRS Leader

Ernst & Young et Associés

Impacts on loan provisionsand related drivers andchallenges ahead

Catriona Early,Regulatory ReportingSpecialist

Ernst & Young LLP

Moratoria, guaranteedloans and the impact ofmacroeconomic scenarios

Marek Rozkrut,Head of Economic Analysisfor Europe

EY Polska

An outlook on themacroeconomicenvironment

An overview of theimpacts on loanprovisions and the keydrivers

Laure Guégan

3 IFRS 9: an analysis of Q2 impacts and expectations ahead


Analysis of the half-year 2020 IFRS 9 impacts

► Benchmark analysis based on the half-year earnings communication► Half-year reports International Accounting Standards (IAS) 34 financial statements,

earnings presentations and Pillar 3 regulatory disclosures► Areas of focus:

► Half-year 2020 expected credit loss (ECL) expense and COVID-19 pandemic effects► Cost of Risk (CoR)/loss rate ratios (ECL expense/gross loans)► Exposures and impairment allowances (movements and stage allocation)► Coverage ratios (ECL allowance/gross loans)► Macroeconomic assumptions (scenarios, weighting) and outlooks

1.183bAverage total assets

534bAvg. gross loans tocustomers

11.1bAverage allowance

14.2%Average CET1 ratio

medianaverage

outlier

25%

25%

25%

25%

Note: all reporting currencies have been converted to Euros for purposes of comparison


ECL in half-year (HY) 2020 already doubles full-year (FY) 2019 ECL expense

ECL expense in HY 2020compared to FY 2019 (€b)

ECL expense FY 2019ECL expense Q1 2020

ECL expense Q2 2020

2.7bAverage HY 2020ECL expense


The Cost of Risk (CoR) in HY 2020 has reached 100bps on average

2019 CoR

Bubble size reflects the proportionof S1 and S2 in the ECL expense

x4 x2

x1

NB. Re-calculated ratios whichmay differ from disclosed CoR

CoR HY 2020 (annualized)compared to FY 2019

ECL expense in HY 2020compared to FY 2019 (€b)

ECL expense FY 2019ECL expense Q1 2020

ECL expense Q2 2020

Average 1.0%

0.4%Average

Q2 2020 compared to Q1 2020► Comparing Q2 2020 to Q1 2020 shows some adjustments and

a slight reduction in the dispersion of banks► The five banks with the highest CoR in Q1 have decreased

(by 60 bp on average)► 10 banks have increased their CoR in Q2 (50bp on average)► A few significant shifts upwards driven by macroeconomic

scenarios (three banks increased by 90bp on average)

Between Q1 and Q2 2020, the dispersion between banks has slightly reduced

7

HY 2020 compared to FY 2019► The annualized CoR/loss rates for HY 2020 are still

scattered across a wide range from 30 bps to 205 bps► Same country trends as in Q1

► Highest levels of CoR: Spain and UK► Highest increase compared to 2019: UK (x 4.6)► Germany and France: x2.5 on average

► Various drivers :► Products► Geographical footprint► Macroeconomic outlook, scenarios and weights► Government support measures► Modeled versus non-modeled impacts► A few banks have significant single-name losses

► Two consistent trends across banks driving the sharpincrease in ECL expense :► Stage 1 (S1) and Stage 2 (S2) increase► Significant overlays

Q2 2020CoR

(annualized)

Q1 2020 CoR(annualized)

55%Q2 proportion of HY2020 ECL expense

IFRS 9: an analysis of Q2 impacts and expectations ahead


The HY20 ECL expense was driven by Stage 1 (S1) and Stage 2 (S2),which represent almost half of the impact

► The sharp increase in S1 and S2 ECL was driven byrevised macroeconomic scenarios and overlays

S1 and S2 portion of ECL expenseHY 2020 compared to FY 2019 (€b)

ECL expense FY 2019: S3ECL expense HY 2020: S1 / S2ECL expense HY2020: S3

ECL expense FY 2019: S1 / S2

3%S1 and S2 inFY 2019 ECL expense

44%S1 and S2in HY 2020 ECL expense


The HY20 ECL expense was driven by Stage 1 (S1) and Stage 2 (S2),which represent almost half of the impact

► The sharp increase in S1 and S2 ECL was driven byrevised macroeconomic scenarios and overlays

► Increased coverage ratio for performing exposures:

S1 and S2 portion of ECL expenseHY 2020 compared to FY 2019 (€b)

ECL expense FY 2019: S3ECL expense HY 2020: S1 / S2ECL expense HY2020: S3

ECL expense FY 2019: S1 / S2

HY 2020

Year-end2019

S1 / S2 ECL allowance / gross loans to customers

Average0.45%

Average0.64%

An increased use of overlays to supplement the models,involving a considerable level of expert judgment

10

► On average, overlays represent 20% of the HY 2020 ECLexpense, ranging from 5% to 65%► Often result in a net increase of the ECL balance, but

with very significant downward effects due to modeladjustments

► Some banks have included detailed disclosuresincluding the amount by main category, the modelsegments impacted and the framework used

► Comparisons are still difficult as overlays vary innature and disclosed amounts include some offsettingeffects

► Some significant overlays booked in Q1 have been“absorbed” by the revision of macroeconomic scenarios► The need for an overlay due to changed

macroeconomic conditions in Q1 was deemed nolonger necessary under the revised scenarios

► In some cases, the significant overlay recorded in thefirst quarter remained but was allocated across thedifferent markets / portfolios

► Banks describe three main types of overlays

1. Model adjustments (“in-model” and “post-model”adjustments)► Generally more significant for wholesale and low-risk counterparties

models► A number of IFRS 9 ECL models are now operating outside the

boundaries to which they were calibrated► Some models extrapolate the recent unseen variations in

macroeconomic loss drivers into unrealistically high default rates► Adjustments are made to more appropriate levels based on historical

benchmarks (peak default rates observed in previous crises andother stress scenario analysis)

► Wider-ranging model changes will take time to develop and needmore real data

2. Effect of government support measures► The mitigating effect of these measures is expected to result in both

a delay and a reduction in the peak level of default and loss rates► Several banks note that macroeconomic variables have been

averaged to ensure an appropriate probability of default (PD) output

3. Sectors► Adjustments have been applied to implement an appropriate

differentiation in the severity of projected default rate conditions fordifferent industry sectors


0

25

50

75

100

125

150

175

200

225

UK

B1

UK

B2

UK

B3

UK

B4

UK

B5

Span

ish

B1

Span

ish

B2

Ital

ian

B1

Ital

ian

B2

Dut

ch B

1

Dut

ch B

2

Fren

ch B

1

Fren

ch B

2

Fren

ch B

3

Fren

ch B

4

Ger

man

B1

Ger

man

B2

Swis

s B1

CoR (1H2020)2020 Outlook (low)2020 Outlook (high)


Outlook suggests increased convergence by year-endbut with different country trends remaining

2020 outlook CoR compared to HY 2020 CoR (as disclosed) – in bps

► The average high end outlook is 105bps,and the low end is around 85bps

► The outlook is generally lower for thebanks with the highest CoR in HY 2020,suggesting they may have frontloadedmore the expected losses

► Two banks have an outlook below HY2020 and four banks have indicated thatthey expect less in the second half

► Three banks indicate a high-end outlookhigher than in first HY 2020 (suggestinga higher expense in the second half)

► For five banks, the outlook is very closeto the first HY 2020

► The outlook always includes a caveatthat it is only relevant if conditions don’tfurther deteriorate

► Some banks haven’t disclosed anyoutlook and stress the high level ofuncertainty in projecting CoR

CoR HY 2020 (annualized)2020 outlook (low)2020 outlook (high)


More banks are now applying the IFRS 9 transitional arrangementsin the calculation of their CET 1 ratio

Benefit of IFRS 9 transitional adjustment at Q2 2020 for CET1 ratio► On average, banks which apply the add-back experience a

benefit of 40 basis points (bps), with a maximum add-back of120 bps reported

► This is higher than the benefit at Q1, reflecting increasedECL allowance in Q2

► This is based on 13 banks that apply the transitionalarrangements (TA), all of which have internal rates-based(IRB) portfolios

► At Q2, there are an additional four banks that have adoptedthe TA. These banks cite the European Central Bank (ECB)

► The majority of banks apply the revised “quick fix” reliefpackage that was adopted into European law in June

0.1%

1.2%

0.8%

0.9%

0.1%

0.4%

0.7%

0.8%

0.1%

0.3%

0.2% 0.2%

0.1%

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

UK

Ban

k 1

UK

Ban

k 2

UK

Ban

k 3

UK

Ban

k 4

UK

Ban

k 5

Span

ish

B 1

Ital

ian

B 1

Ital

ian

B 2

NLD

B 1

Fren

ch B

1

Fren

ch B

2

Fren

ch B

4

Ger

man

B 1

0.0

0.2

0.4

0.6

0.8

1.0

2018 2019 2020 2021 2022 2023 2024 2025

Revised proposal: add-back

Reviseddynamic add-

back

Static add-backand additional

static continues


A significant increase in Stage 2 exposures driven by forward-looking information

Stage 2% of [Stage 1 + Stage 2 exposures] ► On average, the proportion of Stage 2 almost doubles, butwith significant differences reflecting different product mixand how banks incorporate forward-looking information in theassessment of significant deterioration in credit risk (SICR)

► Legislative and non-legislative large scale moratoria did notautomatically trigger Stage 2 transfers

► Most often, banks have not changed their quantitative andqualitative criteria, but supplemented them with targetedsupplemental analysis for vulnerable sectors and borrowerswith weaker ratings before the crisis

2020 Stage 2% of [Stage 1 + Stage 2 exposures]2019 Stage 2% of [Stage 1 + Stage 2 exposures]

7.7%2019 Average Stage 2 %

11.7%Average 2020 Stage 2 %

14

Wholesale portfolios show the largest increase in Stage 2

Retail unsecured - Stage 2% of [S1 + S2 exposures] Retail secured - Stage 2% of [S1 + S2 exposures]


Total retail - Stage 2% of [S1 + S2 exposures] Wholesale - Stage 2% of [S1 + S2 exposures]

15

However, increase in coverage ratio is morepronounced for retail unsecured portfolios

Retail unsecured - coverage ratio (ECL/gross loans) Retail secured - coverage ratio (ECL/gross loans)


Total retail - coverage ratio (ECL/gross loans) Wholesale - coverage ratio (ECL/gross loans)


Stage 2 exposures in arrears have decreased,reflecting the effect of moratoria and other support measures

% Stage 2 exposures in arrears(from Pillar 3 disclosures)

30 days < Past due ≤ 90 days / Stage 2 loans at HY 202030 days < Past due ≤ 90 days / Stage 2 loans at FY 2019

► All banks stress that moratoriaand other support measures have“turned-off” some usual SICRtriggers

► Legislative and non-legislativelarge scale moratoria did notautomatically triggered Stage 2transfers

► The significant differences inStage 2 increase at HY 2020reveals differences in how banksincorporate forward-lookinginformation in SICR assessment

► Further transfers in Stage 2 aretherefore expected in the nextquarters when the moratoriacome to an end


Stage 3 ratio remained relatively stable,also reflecting the effect of moratoria and other support measures

Stage 3 % of total loans to customers ► On average, the proportion of Stage 3increased slightly from 2.84% to 3%

► The dispersion is still significant (from 1% to7%) but has decreased over the past years

► Legislative and non-legislative large scalemoratoria have not automatically beenconsidered as forbearance measures anddid not trigger a Stage 3 transfer

► Banks have to monitor carefully thetransition from moratoria to “back tonormal” to avoid massive shifts in Stage 3

2020 Stage 3 % of total loans to customers2019 Stage 3 % of total loans to customers

3%Average 2020 Stage 3 %

Moratoria, guaranteedloans and the impact ofmacroeconomic scenarios

Catriona Early


26

60

17

26

6

91

33

8

18

7 3 4

17

4

-

5

4

8

1

12

7

-

21

25

33 21

40

4

6

6

13

8

27

6

8 10

1315

3

6%

14%

6%

12%

5%

13%12%

7%

10%

3%

7%6%

7%

1%

4%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

-

20

40

60

80

100

120

UK Bank 1 UK Bank 2 UK Bank 3 UK Bank 4 UK Bank 5 Spanish B 1Spanish B 2 Italian B 1 Italian B 2 NLD B 1 NLD B 2 French B 1 French B 2 German B 1German B 2

% of

boo

k

Curr

ency

uni

t, b

Households SMENon-financial corporations (non-SME) Other% of total loans and advances subject to moratorium

Banks have started to disclose quantitative informationon moratoria and guaranteed loans - moratoria


Legislative and non-legislative moratoria

► Some significant differences in the allocationof moratoria between households, small andmedium sized entities (SME) and othercorporate companies

► On average, moratoria represents 7% of totalloans but with significant differences (from1% to 4% in Germany to 13% in Spain)

► The highest proportion of moratoria tend tobe more concentrated on households withlower average exposures (15-20k€ onaverage in Spain)

► Generally, the expected maturity is sixmonths, revealing a high level of uncertaintyin the short-term

► A number of banks stress that a number ofobligors have already resumed normalpayments revealing the efficiency of thesetemporary measures for at least a portion ofthe obligors

Loans and advances subject to legislative and non-legislative moratoria(based on Pillar 3 disclosures)

2.51.1 1.2 0.6

7.5 7.9

5.4

20.5

7.73.1

6.0

9.9

0.7 0.9 1.5

10.6

0.5 0.4

4.4

6.1

7.0

2.5

3.1

4.9

0.5 0.5

0.8

0

5

10

15

20

25

UK Bank 1 UK Bank 2 UK Bank 3 UK Bank 4 UK Bank 5 Spanish B 1 Spanish B 2 Italian B 1 Italian B 2 NLD B 1 NLD B 2 French B 1 French B 2 German B 1 German B 2 CH B 1

Curr

ency

uni

t, b

n

Households SME Non-financial corporations (non-SME) Other

Banks have started to disclose quantitative informationon moratoria and guaranteed loans – guaranteed loans


Guaranteed loans

► The amount of guaranteed loansreveal different implementationframeworks across countries: differenttargets (SME vs. other corporates),different structure (fully or partiallyguaranteed) and speed of execution

► In some countries, the guaranteedloans are passed-through to a state-owned bank and not reported on thebalance sheet

► In some schemes, no payment isrequired in the first year, resulting inthe same effect as moratoria (usualsigns of deterioration temporarilyturned-off)

Newly originated loans and advances provided under newly applicable public guarantee schemes (b€)(based on Pillar 3 disclosures)

0

2

4

6

8

10

12

14Agriculture and food

Automobiles and components

Aviation

Commodity traders

Construction, real estatedevelopment and

infrastructure

Health

Hospitality, leisure andtourismMetals and mining

Oil and gas

Retail (non-food retail)

Sports, Education and Media

Textiles

Transportation and logistics(excl. airlines)

Number of banks citing specific vulnerable exposures

No. of banks citing exposure, Q1 No. of banks citing exposure, Q2

Corporate exposures: vulnerable sector analysis

► Most banks (80%) provide significant informationon vulnerable sectors in the earnings presentation

► A number of banks stress that their sectorvulnerability is magnified by the COVID-19pandemic

► The content and format of information variessignificantly:► Sector coverage► Exposure values for vulnerable, high risk or

noted sectors► Interaction with geography► Credit quality and credit risk mitigation► Outlook (sensitivity analysis)

► Significant diversity in IFRS 9 ECL approaches:► Stage 2 transfers (individual or portfolio

approach)► Stressed ECL► Overlays

► Close interaction with macroeconomic outlookand expected impact of support measures


Source: European Commission (EC), Summer 2020 Economic Forecast, July 2020; European Central Bank, Eurosystem Staff Macroeconomic Projections, September 2020; EuropeanCentral Bank, Eurosystem Staff Macroeconomic Projections, June 2020; Bank of England (BoE), Monetary Policy Report, August 2020; Bank of England, Monetary Policy Report, May2020

22

Annual GDP growth in the Eurozone (2019=100) Annual GDP growth in the UK (2019=100)

85

90

95

100

105

2019 2020 20222021

German B1

French B3French B1

Italian B1

ECB mild (Sep 2020)ECB baseline (Sep 2020)ECB severe (Sep 2020)ECB baseline (Jun 2020)

85

90

95

100

105

20222019 2020 2021

UK B1

UK B3

BoE (Aug 2020)

UK B4

BoE (May 2020)EC (Jul 2020)

ECB mild

ECB severe

Observations on forecasts disclosed by banksGDP forecasts compared to regulators revised forecasts

UK B2

French B4

0%

10%

20%

30%

40%

50%

60%

70%

80%

1 2 3 4 5

Wei

ghti

ng

Scenarios

Observations on forecasts disclosed by banksBanks have often adjusted their approach to multi-economic scenarios


► Banks have often adjusted their approach to multi-economic scenarios, some have removed some scenarios (now considered unrealistic)and others have increased the number of scenarios to reflect the increased level of uncertainty around the baseline

► The weight allocated to the baseline is on average 54% (compared to 51% at year-end 2019) but there are some significant differences :► Two banks increased it from 30% or 42.5% to 70%► Three banks kept it stable at 30%, 50% or 60%► Three banks decreased it, including the two banks with the highest weights in 2019 (from 80% and 74% to 60% and 65%)

Scenarios weightings year-end 2019 Scenarios weightings half-year 2020

0%

10%

20%

30%

40%

50%

60%

70%

80%

1 2 3 4 5

Wei

ghti

ng

ScenariosUpper case 1 Upper case 2 Base case Lower case 1 Lower case 2Upper case 1 Upper case 2 Base case Lower case 1 Lower case 2

The incremental reported ECL due to multiple economic scenariosremains a relatively small proportion of baseline ECL


9% 8% 8%

16%

27%

4%

105%

56%60%

53%

89%

15%

0%

20%

40%

60%

80%

100%

120%

UK Bank 1 UK Bank 2 UK Bank 3 UK Bank 5 CH B 1 NLD B 2

Sensitivity to macroeconomic scenarios% increase in ECL allowance

Reported versus Central (% Central)

Most downside versus Reported (% Reported)

► Banks stress there was an increasein ECL sensitivity observed in allscenarios

► A few banks disclose the ECLallowance calculated under severalmacroeconomic scenario weightedat 100%

► The reported ECL is theprobability-weighted outcome ofthese different modeled ECL

► On average, the reported ECLincreases the baseline ECLbalance (based on 100%weighting) by 12%

► On average, the most downsidescenario (based on 100%weighting) would increase thereported ECL balance (probability-weighted) by 63%

central (% central)

reported (% reported)

An outlook on themacroeconomicenvironment

Marek Rozkrut


Many economies contracted already in Q1, with further deterioration in Q2 2020

26

Source: Eurostat; local statistical offices. * Data not seasonally adjusted

4.4

2.4

0.8 0.7 0.3

-0.1 -0.2 -0.4 -0.5-1.2 -1.5 -1.7 -2.2 -2.3 -2.4 -2.8 -3.2

-3.7 -3.7 -4.1

-5.6 -5.7

Latv

ia

Lith

uani

a

Fran

ce

USA

Irel

and

Ger

man

y

Net

herl

ands

Cypr

us

Slov

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Luxe

mbo

urg

Esto

nia

Mal

ta

Gre

ece

Finl

and

UK

Euro

zone

Port

ugal

Belg

ium

Aus

tria

Slov

akia

*

Spai

n

Ital

y

a) GDP growth in Q1 2020 (% YOY) in the Eurozone, UK and US – seasonally adjusted data

b) GDP growth in Q2 2020 (% YOY) in the Eurozone, UK and US – seasonally adjusted data

-3.7 -4.0-6.3 -6.5

-8.6 -9.1 -9.2-11.3 -11.9 -12.1 -12.9 -12.9

-14.4 -14.7 -15.2 -15.2 -16.3-17.7 -18.9

-21.7 -22.1

Irel

and

Lith

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Ger

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Net

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Latv

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*

Aus

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Belg

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Euro

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Gre

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Port

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Fran

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Spai

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However, GDP contraction in Q2 2020 was less severe than forecasted – with theexception of Spain and UK

27

Source: Eurostat (September 8, 2020); European Commission, Summer 2020 Economic Forecast, July 2020

8.07.0

5.7

3.93.4 3.3 3.0 2.9

2.4 2.3 1.91.5

0.4 0.3 0.2

-1.5

-3.2

Belg

ium

Irel

and

Lith

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Esto

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Slov

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Latv

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Finl

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Net

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Euro

zone

Slov

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Port

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Ger

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UK

Difference for GDP growth in Q2 2020 between the initial Q2 2020 realization and the last forecast of the European Commissionin percentage point (PP)


But no Eurozone member state is expectedto escape a deep recession in 2020

28Source: European Commission, Summer 2020 Economic Forecast, July 2020; European Central Bank, Eurosystem Staff Macroeconomic Projections, September 2020; IMF, WorldEconomic Outlook, June 2020 (for the US). * ECB projections (September 2020)

-0.1

-0.9

-1.0

-1.1

-1.3

-1.3

-1.9

-2.0

-2.3

-2.5

-2.7

-2.8

-2.9

-3.1

-3.4

-3.5

-3.7

-3.8

-3.9

-4.3

-4.4

-4.6

-5.8

Italy

Luxe

mbo

urg

Ger

man

y

Mal

ta

Latv

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nia

Aus

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Esto

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Fran

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Euro

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Gre

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Finl

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USA U

KPo

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ain

Euro

zone

*

b) Cumulative GDP growth in 2020-2021 (%)a) GDP growth in 2020 (% year on year (YOY))

-6.0

-6.2

-6.3

-6.3

-6.8

-7.0

-7.0

-7.1

-7.1

-7.7

-7.7

-8.0

-8.0

-8.5

-8.7

-8.8

-9.0

-9.0

-9.7

-9.8

-10.

6-1

0.9

-11.

2

Slov

akia

Mal

taLu

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bour

gFi

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Aus

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ethe

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COVID-19 takes its economic toll: Eurozone experiencing a deep recession in 2020

Source: Eurostat (historical data); European Commission, Summer 2020 Economic Forecast, July 2020; Organisation for Economic & Cooperative Development (OECD), EconomicOutlook, June 2020; European Central Bank (ECB), Eurosystem Staff Macroeconomic Projections, September 2020; World Bank (WB), Global Economic Prospects, June 2020 ;International Monetary Fund (IMF), World Economic Outlook, June 2020. * Seasonally adjusted data

-9

-6

2019 202220212020

-3

0

6

3

9

Eurostat

ECBEC

OECDWBIMF

-15

-10

-5

0

5

10

15

20

Q1 2019 Q1 2021Q1 2020 Q1 2022

ECEurostat

OECD*ECB*

a) Quarterly GDP growth in the Eurozone (% YOY) b) Annual GDP growth in the Eurozone (% YOY)

29

d) Cumulative GDP growth in the Eurozone in 2020-2021 (%)c) GDP growth in the Eurozone (Q4 2019 = 100)

80859095

100

Q4 2019 Q2 2022Q2 2020 Q4 2022Q4 2020 Q4 2021Q2 2021

EC

ECB*OECD*

-3.1 -3.1 -3.2 -3.4-5.0

ECB(Sep 2020)

OECD(Jun 2020)

EC(Jul 2020)

IMF(Jun 2020)

WB(Jun 2020)

The impact of economic lockdown on GDP growth depends, among other things, on theeconomic structure, the number of disease cases and stringency of social distancing thataffect community mobility

30 Source: Eurostat; Google COVID-19 Community Mobility Reports; Oxford COVID-19 Government Response Tracker; https://www.worldometers.info/coronavirus/

4 90 1 52 7 103

-20

6 8-25

-10

-15

-5

0

HR

COVID-19 cases per one thousand inhabitants

BG

MTSI

NL

FI

AT

LT

RO

GDP

gro

wth

in 2

Q20

20(%

yoy

)

BE

CYCZ

DKPL

FR

DEGRHU

IT

EE

PT

SK

ES

SE

GB

LV

-45-15-10 -35

-20

-70

-5

0 -50-40 -65-60-5 -30 -55-25

-25

-15

-20

-10

0

Average mobility in retail and recreation in Q2 2020 (change vs. pre-pandemic period)

MT

HU

DEDKLV

GDP

gro

wth

in Q

2 20

20(%

YO

Y)

ATBE

BG

HRGR

CZ

EE LTFI

RO

FRIT

PL

NL

GB

SKSI

ES

SE

PT

5228 32 6836 4844

-10

-15

40-25

6056

-20

64

-5

0

FRIT GR

Government policy stringency index in Q2 2020 (quarter on quarter change)

BESIHU

ESGDP

gro

wth

in 2

Q20

20(%

yoy

)

AT

BG

HR

CY

ROCZ

PT

DKSE

EEFI

GB

DE

IELV

LT

NL

PL

SK

-15

8% 10%4%2%

-5

6%

-10

-25

-20

0

PT

PL

Share of sectors (in total VA):1) Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment,

2) Accommodation and food service activities, 3) Arts, entertainment and recreation

EE

HU

DK

FR

GDP

gro

wth

in Q

2 20

20(%

YO

Y)

AT

BE

BG

SIHR

LV

IT

CY

CZ

FI

GR

IE

NL

LT

DESK

ES

SE

GB

RO

y = -1.3678x - 3.4737R² = 0.3519

y = -1.0007x - 9.7556R² = 0.2667

y = -0.3605x + 5.1799R² = 0.1585

y = 0.2818x - 1.9948R² = 0.6668

a b

dc

The impact of economic lockdown on GDP growth depends, among other things, on theeconomic structure, the number of disease cases and stringency of social distancing thataffect community mobility

31 Source: Eurostat; Google COVID-19 Community Mobility Reports; Oxford COVID-19 Government Response Tracker; https://www.worldometers.info/coronavirus/

4 90 1 52 7 103

-20

6 8-25

-10

-15

-5

0

HR

COVID-19 cases per one thousand inhabitants

BG

MTSI

NL

FI

AT

LT

RO

GDP

gro

wth

in Q

2 20

20(%

YO

Y)

BE

CYCZ

DKPL

FR

DEGRHU

IT

EE

PT

SK

ES

SE

GB

LV

-45-15-10 -35

-20

-70

-5

0 -50-40 -65-60-5 -30 -55-25

-25

-15

-20

-10

0

MT

HU

DEDKLV

GDP

gro

wth

in Q

2 20

20(%

YO

Y)

ATBE

BG

HRGR

CZ

EE LTFI

RO

FRIT

PL

NL

GB

SKSI

ES

SE

PT

-15

8% 10%4%2%

-5

6%

-10

-25

-20

0

PT

PL

Share of sectors (in total VA):1) Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment,

2) Accommodation and food service activities, 3) Arts, entertainment and recreation

EE

HU

DK

FR

GDP

gro

wth

in Q

2 20

20(%

YO

Y)

AT

BE

BG

SIHR

LV

IT

CY

CZ

FI

GR

IE

NL

LT

DESK

ES

SE

GB

RO

y = -1.3678x - 3.4737R² = 0.3519

y = -1.0007x - 9.7556R² = 0.2667

y = 0.2818x - 1.9948R² = 0.6668

► Changes in mobility are more related to the number ofCOVID-19 cases rather than stringency of governmentpolicy

► While another nationwide lockdown is unlikely (not partof the baseline scenario) and survival rates of ICUpatients with COVID-19 have improved, a recentincrease in the number of new COVID-19 cases and alikely second wave are important risk factors

► Vaccine or an effective treatment would be game-changers

Average mobility in retail and recreation Q2 2020 (change vs. pre-pandemic period)

a b

c

The role of sectors most affected by the lockdown varies among Europeancountries

32

Source: Eurostat. *or latest available

0

1

2

3

4

5

6

Mal

ta

Spai

n

Net

herl

ands

Italy

Belg

ium

Ger

man

y

Cypr

us

Slov

akia

Finl

and

Euro

zone

Slov

enia

Lith

uani

a

Aus

tria UK

Fran

cePo

rtug

al

Esto

nia

Latv

iaG

reec

eIr

elan

d

Luxe

mbo

urg

2018

0

1

2

3

4

5

6

7

8

9

Cypr

us

Italy

Latv

ia

Spai

n

Port

ugal

Gre

ece

Aus

tria

Euro

zone UK

Fran

ce

Luxe

mbo

urg

Slov

enia

Esto

nia

Net

herl

ands

Irel

and

Finl

and

Ger

man

y

Lith

uani

a

Belg

ium

Slov

akia

Accommodation and food service activitiesArts, entertainment and recreation

a) Accommodation and food service activities; arts, entertainment andrecreation (% of total value added in 2018*)

b) Manufacture of motor vehicles, trailers, semi-trailers and of othertransport equipment (% of total value added in 2018*)


… which partly explains differences in the performance of Eurozone economies

Contribution of value added generated by groups of sectors to the change in total value added in Q2 2020 (YOY, PP)

33

FranceEurozone Germany Netherlands-22.9

Italy

-14.7

UKSpain

-9.3

-18.9

-11.4

-22.0

-18.0

AgricultureIndustry

Information and communication

Business services

Finance

ConstructionTrade, transport, accommodation and restaurants

Real estate

Public administrationArts, entertainment and recreation


Source: Eurostat

Unprecedented rescue packages adopted by governments significantly reduced the impact of theCOVID-19 pandemic on the labour market, which supported consumption

34

%

a) Annual change in employment and hours worked in Q2 2020

-2

0

2

4

6

8

10

12

Ger

man

yBe

lgiu

mJa

pan

Mal

taN

ethe

rlan

dsA

ustr

iaIta

lyRo

man

iaFr

ance UK

Finl

and

Euro

are

aEU

27Cy

prus

Swed

enPo

land

Hun

gary

Port

ugal

Slov

enia

Czec

hia

USA

Den

mar

kCr

oatia

Bulg

aria

Slov

akia

Gre

ece

Irel

and

Spai

nLi

thua

nia

Esto

nia

Latv

ia

2008 2020* **

b) Change in unemployment rate during the global financial crisis andduring the COVID-19 pandemic*

PP

Source: Eurostat; *maximum level of unemployment rate in 2010 compared to September 2008 level; **latest available data on unemployment rate (May-July 2020) compared to January 2020 level

-35

-30

-25

-20

-15

-10

-5

0

5

10

Gre

ece

Spai

nPo

rtug

alIta

ly UK

Fran

ceSl

ovak

iaEu

ro a

rea

Hun

gary

Cypr

us EUSl

oven

iaEs

toni

aIc

elan

dLi

thua

nia

Mal

taBu

lgar

iaCz

echi

aIr

elan

dLa

tvia

Ger

man

yN

ethe

rlan

dsLu

xem

bour

gD

enm

ark

Aus

tria

Swed

enFi

nlan

dN

orw

ayRo

man

iaPo

land

Croa

tiaemployment hours worked

► Rescue packages, adopted to protect companies and jobs, will result in record high government debts► Government policy will continue to play a critical role: extending vs. withdrawing stimulus measures


Retail trade rebounded quickly (partly an effect of pent-up demand), but industrial production andconstruction remain significantly below the pre-pandemic trend


Source: Eurostat

65

70

75

80

85

90

95

100

105

110

20202018 2019

-7%

a) Industrial production in Eurozoneby July 2020 (Feb 2020=100)

2018 2019 2020

-2%

b) Retail trade turnover in Eurozoneby July 2020 (Feb 2020=100)

65

90

70

75

80

95

85

100

105

110

2018 2019 2020

-7%

c) Construction in Eurozoneby June 2020 (Feb 2020=100)

The impact of the COVID-19 pandemic has varied among Eurozone economies

Source: Eurostat

Feb 20 Apr 20 Jun 20

a) Industrial production in selected countriesby July 2020 (Feb 2020=100)

36

Feb 20 Apr 20 Jun 20

b) Retail trade turnover in selected countriesby July 2020 (Feb 2020=100)

100

30

40

50

60

70

80

90

110

Apr 20Feb 20 Jun 20

c) Construction in selected countriesby July 2020 (Feb 2020=100)

Eurozone

FranceGermany

ItalySpainNetherlandsUK

EurozoneGermany

Netherlands

ItalyFrance

Spain

UK

EurozoneGermany

Netherlands

ItalyFrance

UK

Spain


30

40

50

60

70

80

90

100

110

… and also among sectors and retail trade categories: from an economy-wide tosector recession

a) Volume of production in manufacturing inselected sectors, Euro area (Feb 2020=100)

37

b) Retail trade turnover in selected categoriesof stores, Euro area (Feb 2020=100)

c) Turnover in selected services categories,Euro area, (Feb 2020=100)


0

20

40

60

80

100

120

Mar-20 May-20 Jul-20

Pharmaceuticals

Food and beverages

Motor vehiculesFurniture

Wearing apparel

Consumer electronics Food, beverages, tobacco

Textiles, clothing, footwareMedical, cosmetic, toilet articles

Household equipment, cultural and recreation goods

Computers, telecommunications equipment

0

20

40

60

80

100

120

Publishing

Land transport

Accommodation

Programming and broadcasting

Food and beverage services

Travel agencies, tour operators

Source: Eurostat

Feb-20 Apr-20 Jun-20 Mar-20 May-20 Jul-20Feb-20 Apr-20 Jun-20 Mar-20 May-20 Jul-20Feb-20 Apr-20 Jun-20

Many Eurozone countries are relatively strongly exposed to disruptions ininternational value chains

38

Source: EY Global Value Chain Model calculations based on updated World Input-Output data (2018)

155

20

250

30

300

10 20

35

35

40

40 45

25

45

50

55

70

60

65

5

10

15

DEU

HUN

HRV

FINMEX

POL

GRC

Share of foreign value added incorporated in gross output (production) in 2018 (%)

LUX

KOR

Shar

e of

dom

esti

c va

lue

adde

d ab

sorb

ed a

broa

d in

201

8(%

)

MLT

IRL

NLD

AUT

CZE

SVK

BELDNK

LTU

UK

EST

TWN

FRA

BGR

ROU

LVA

SVN

SWE

PRT

CHE

CANTUR

NOR

ROWESP

CYP

IDNAUS

JPNCHN

BRA

RUS

USAIND

ITA

The pace of recovery in many Eurozone economies will be strongly affected by changes in external demand

Eurozoneother countries

USAUK

45-degree line


39

Key considerations underpinning macroeconomic estimates for banks


► Incorporation of actual data into base case and scenarios/simulations► Consideration of:

► Number of new COVID-19 cases and the risk of second wave► Medical developments (effective treatment / vaccine)► Government support programmes: extending / withdrawing stimulus measures► Consumer and corporate responses (community mobility, confidence indicators, high-frequency data)

► Time profile — return to trend, level of trend given structural shifts► Sector and country specific analysis, including the role of external demand and exposure to supply

chain disruptions► Scenario weights, confidence in base case, balance of risks► Comparison to regulator “scenarios”

Summary and challengesahead

Laure Guégan


Challenges ahead


► Adjust and stabilise the process for estimating ECL in a new paradigm involving an increased level ofuncertainty and judgment► Unprecedented health and economic crisis with unprecedented level of government support measures

► Many unknowns: length and magnitude of the pandemic and the government support measures as well as middle and long-termconsequences on the economy

► Risk models have not been developed to provide reliable estimates under these previously unseen circumstances► Usual model correlations and calibration require some adjustments (macroeconomic models, PD models, loss given default (LGD))► Model recalibration requires observation of the full impacts of the COVID-19 pandemic are observed and will require more time

► The fast-moving environment requires more agility in the IFRS 9 processes to incorporate new information► High quality governance is critical around key areas of judgment (scenarios, model adjustments, overlays)

► Manage the transition from existing, widely available COVID-19 specific payment deferrals to tailoredforbearance arrangements► Prudential Regulation Authority (PRA) statement on COVID-19: IFRS 9 and capital requirements – guidance as COVID-19 specific

payment deferrals come to an end (26 August 2020):“At the end of the existing, widely available COVID-19 specific payment deferrals, if the borrowers involved are not able to resumepayments in full immediately with all deferred sums either paid in full or capitalised, tailored forbearance arrangements (…) should beconsidered. Such tailored forbearance arrangements are likely to be as good an indicator of SICR, credit impairments or defaults asforbearance was prior to the pandemic.”

Challenges ahead - how to assess significant deterioration of risk

Individual risk analysis

Because large-scale moratoria and guaranteed loans tend to“turn off” usual SICR backstops (forbearance, days past due),banks need to:► Consider other indicators to determine whether the

borrower’s difficulties are temporary (e.g., forced leave)versus longer-term (e.g., they have been made redundant)

► Define appropriate monitoring for new guaranteed loans► Assess implementation of governmental initiatives and to

what extent they will limit the defaults► Banks are likely to have less information for retail than

wholesale customers and need to design a holistic approach :► Economic conditions► History of missed payments or adverse credit bureau scores► Current data (employment status, current account activity etc.)► Use of portfolios approaches and application of expert judgment

► For wholesale exposures, ratings tend to react more quicklybased on updated financial information. However, portfolioand sectorial approaches remain critical

► Pooling of loans should consider factors such as:► Products: mortgages vs. retail unsecured loans, etc.

► Type of relief measures granted, initially and subsequently► Whether the borrower is asking for a renewal of a payment

holiday or it is a first request

► Geography (e.g., country-wide support measures)► Industry (travel, hospitality, entertainment and services

industries)

► Behavioral information (historic and current)► Information collected when granting or renewing payment

holidays► Revised macroeconomic assumptions should be incorporated

in the assessment► Overlay approaches may be needed to capture the

uncertainty, limitations of historical data, sector-specific risksand possible government actions

Collective or sectorial approaches


Challenges ahead - key areas of IFRS 9 ECL disclosures

43

► How the COVID-19 pandemic hasbeen incorporated in themacroeconomic scenarios

► How new scenarios compare to theprevious ones

► Weights and underlying rationale► Sensitivity analysis and outlook for

the full year

► How government relief measureshave been reflected

► Overlays (articulation with Stage 2transfers and risk parameters)

► If and how the usual IFRS 9 ECLestimate process and governance ofthe bank has been adjusted for thepurpose of the half-year accounts

► Main features of the schemesimplemented by the bank: state-guaranteed loans, public/privatemoratoria (with or without waiver ofinterests)

► Accounting analysis of the schemes :► Initial fair value (FV) of the loans► Effective interest rate calculation► Effect of guarantees (integral or not)► Modification accounting

► Related exposures for the bank► Specific risk monitoring approaches► Expected effect of unwinding of public

and private moratoria► Expected impact on arrears► Expected impact on Stage 2 transfers

► Stage 3 losses (including single names)► COVID-19 pandemic scenarios

► Movements in “days past due” andarticulation with moratoria

► Vulnerable sectors: concentrations,portfolio approaches, overlays

► Stage 2 transfers:► How much and why?► Triggers: delinquency? forbearance? other?► Exceptions applied to usual indicators

► Portfolio approaches► Segmentation► Stage 2 transfers/adjusted risk parameters

► Overlays► Why, how and how much?► Product/business allocation► Movements

Macroeconomic scenarios andassumptions:

COVID-19 pandemic loans andgovernment relief measures: ECL movements and outlook


THANK YOU…..and our next EY webcast in the ‘Accounting and regulatory’ series

44

The next EY webcast in this series will be: “The next and beyond in credit risk and capital management and scenario planning”We will cover:► the impact of the current environment on the future of credit risk and capital management► the link to stress testing and overall scenario planning► the impact on data, modeling, process and control considerations, including auditor expectations for the upcoming year-end► looking beyond to how will integrate across their loan loss, capital, stress testing and climate risk management activities

Invites will be distributed closer to the time of the event.


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