RETAIL CREDIT OUTLOOK - TransUnion CIBIL · 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018 Q1 2019 Q2 2019 Q3 2019 Q4 2019 Q1 2020 Q2 2020 Q3 2020 Q4 2020 te

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RETAIL CREDIT OUTLOOK

Anticipating and preparing for the

COVID-19 impact on retail credit market


Key questions that the market is asking, and what we hope to

cover in this presentation

Key implications for lenders1How might the operating

environment change for lenders?

2What can be the potential impact

on retail credit growth?

What may be the likely impact on

asset quality? 3

Future Readiness

Lending Strategy

Risk Management


Operating Environment

How COVID-19 and ensuing containment and relief measures might change

the lending ecosystem?


COVID-19 has affected more than 6 million people across 210

countries around the world

Source: Our World in Data

As on 2nd June 2020

COVID-19 Confirmed Cases


Spread of COVID-19 in India has been relatively slower

compared to other major countries affected by the pandemic

1,000

10,000

100,000

1,000,000

1 11 21 31 41 51 61 71 81

# C

onfirm

ed C

ases

(Log s

cale

)

Days since the 1000th confirmed case

Number of Confirmed COVID-19 cases

Source: Our World in Data

USA

RussiaUK

Spain

Brazil

India

As on 2nd June 2020


The phased lockdown implemented to curb the spread of

COVID-19 has social, financial and economic implications

Social

• Loss of job for daily wage earners and migrant workers

• Migration of labor leaving them struggling to make ends meet

• Anxiety as a result of social distancing, uncertainty, fear of economic recession

Financial

• Impact on consumers’ financial position on account of pay cuts / layoffs

• Revenue reduction for companies leading to potential liquidity challenges for

businesses and solvency crises

• Falling stock prices and widening of credit spreads

Economic

• Hit on consumption demand – Decrease in consumption, reduction in

discretionary spending, postponement of new investments

• Impact on supply side – Decrease in labor supply, curtailment of production, hit

on distribution of goods and services


Labor market conditions have been impacted severely

42.8% 43.0% 42.3% 42.6%38.2%

7.0% 8.2% 7.2% 7.8%

23.5%

0%

10%

20%

30%

40%

50%

May-1

9

Jun-1

9

Jul-19

Aug-1

9

Sep-1

9

Oct-

19

Nov-1

9

Dec-1

9

Jan-2

0

Fe

b-2

0

Mar-

20

Ap

r-20

May-2

0

Pe

rcen

tage

Labor Participation and Unemployment

Labor Participation Rate Unemployment Rate

Source: CMIE


Consumer sentiment has taken a hit as a result of worsening

economic conditions

108.3 105.7 105.9 105.3

30.9

0

20

40

60

80

100

120

May-1

9

Jun-1

9

Jul-19

Aug-1

9

Sep-1

9

Oct-

19

Nov-1

9

Dec-1

9

Jan-2

0

Fe

b-2

0

Ma

r-20

Apr-

20

May-2

0

Index V

alu

e

Consumer Sentiment Index

Source: CMIE


The pandemic has brought nearly all activity to a standstill,

with the effect more pronounced in the services sector

52.7

51.4 51.2 54.5

30.850.2

52.4 52.7 57.5

12.6 0

10

20

30

40

50

60

70

May-1

9

Jun-1

9

Jul-19

Au

g-1

9

Sep-1

9

Oct-

19

No

v-1

9

Dec-1

9

Jan-2

0

Fe

b-2

0

Mar-

20

Apr-

20

Ma

y-2

0

Index V

alu

e

Purchasing Managers’ Index (PMI)

Manufacturing PMI Services PMI

Source: CMIE


Revenue of most businesses has seen a drop and may fall

further in the short term

0

200

400

600

800

1,000

1,200M

ar-

19

Apr-

19

May-1

9

Ju

n-1

9

Jul-19

Aug-1

9

Sep-1

9

Oct-

19

Nov-1

9

Dec-1

9

Jan-2

0

Fe

b-2

0

Mar-

20

INR

Bn

GST Collections

Source: Government of India


Consequently, India’s economic growth is expected to contract

in 2020

7.5%8.7%

5.6%4.1%

6.2%

1.1%

-12%

-8%

-4%

0%

4%

8%

12%

Q42016

Q12017

Q22017

Q32017

Q42017

Q12018

Q22018

Q32018

Q42018

Q12019

Q22019

Q32019

Q42019

Q12020

Q22020

Q32020

Q42020

YoY

Gro

wth

Rate

Growth in Real GDP

GDP Actual GDP Forecast (pre COVID-19) GDP Forecast (post COVID-19)

India’s GDP estimates have been revised for FY18, FY19 and FY20

~INR 24

trillion(current

prices)

Source: Oxford Economics


The Indian government has announced an economic relief

package of INR 20 trillion under “Atmanirbhar Bharat Abhiyan”

ReformsLiquidity

InfusionInfra

Infrastructure

Push

Helping Stressed

Businesses

• New definition of

MSMEs

• Agri marketing reforms

• Coal, minerals

liberalization

• Higher FDI in defense

production

• Airport, DISCOM

privatization

• New policy for PSUs

• Reduction in CRR

• Collateral free loans /

subordinate debt /

equity for MSMEs

• Special liquidity and

partial guarantee for

NBFCs

• Funds for DISCOM

• EPF support

• INR 2.3 trillion extra

credit to farmers

• Affordable rental

housing for migrants

• Extension of middle

income housing

scheme

• Agri infrastructure

fund

• Higher VGF for social

infrastructure

• Relaxation in

insolvency law

• Expediting tax

refunds

• Funds for stressed

NBFCs

• Moratorium on loan

repayments


India's economic relief package, intended to help spur near

term growth and spending, is amongst the largest in the world

0%

5%

10%

15%

20%

25%Japan

US

Austr

alia

Can

ada

India

Bra

zil

South

Afr

ica

UK

Fra

nce

Tu

rkey

Germ

any

Italy

Indo

nesia

Arg

entina

Russia

Saud

iA

rabia

Chin

a

South

Kore

a

Mexic

o

% o

f G

DP

Economic Relief Packages by G20 Countries

Source: IFC, CSIS, VOX


The pandemic has created operational challenges for lenders

to re-consider and potentially change their operating model

Distribution

• Realigning branches and loan centres to support social distancing guidelines

• Adjusting working hours, staffing mix and times to avoid contamination

• Encouraging customers to use digital channels

• Automating routine service requests (chatbots, etc.)

Customer

Management

• Providing temporary relief to customers without impact on credit history

• Creating customer awareness on support and relief measures

• Addressing evolving needs of customers

• Segmenting customers based on their credit behavior

Internal

Operations

• Automating regular tasks and processes

• Rebalancing workload across operational sites

• Enabling online sanction and disbursement of loans

• Reviewing financial health and BCP plans of third-party service providers


• The lockdowns implemented to curb the spread of COVID-19, and the virus itself, would have

far reaching implications on Indian economy

• Consumers’ financial positions are likely to change dramatically and many companies may

see a reduction in revenue

• Drop in consumer sentiment, significant hit on consumption demand and spending will have a

bearing on the future trajectory of the retail credit market

• Lenders will need to innovate and redesign their operating model to transact with confidence

and better support consumers during these unprecedented times

To summarize:


Credit Growth

What can be the potential impact on demand for major products and the

ability and willingness of lenders to extend credit?


Retail credit growth, which is a reflection of wider economic

activity, has contracted in the last two years

0%

3%

6%

9%

12%

0%

10%

20%

30%

40%

Q12015

Q32015

Q12016

Q32016

Q12017

Q32017

Q12018

Q32018

Q12019

Q32019

Q12020

YoY

GD

P G

row

th R

ate

YoY

Reta

il C

redit

Bala

nces G

row

th R

ate

Growth in Retail Credit Balances and Real GDP

Retail Credit GDP

Products considered: home loan, LAP, auto loan, two-wheeler loan, commercial

vehicle loan, construction equipment loan, personal loan, credit card, business loan,

consumer durable loan, education loan and gold loanSource: TransUnion CIBIL consumer database,

Oxford Economics

Q1 2020 retail credit growth number is as of February 2020


Lending activity has been impacted severely, with some

revival seen in May

0

50

100

150

200

250

Apr-

18

May-1

8

Jun-1

8

Jul-

18

Aug-1

8

Sep-1

8

Oct-

18

Nov-1

8

Dec-1

8

Jan-1

9

Fe

b-1

9

Mar-

19

Apr-

19

Ma

y-1

9

Jun-1

9

Jul-

19

Aug-1

9

Sep-1

9

Oct-

19

No

v-1

9

Dec-1

9

Jan-2

0

Fe

b-2

0

Mar-

20

Apr-

20

May-2

0

Indexed V

olu

mes

Inquiry and Origination Volumes

Inquiry Originations

Products considered: home loan, LAP, auto loan, two-wheeler loan, commercial

vehicle loan, construction equipment loan, personal loan, credit card, business loan,

consumer durable loan, education loan and gold loan

Source: TransUnion CIBIL consumer database

Index: April-18 = 100


Credit growth is a function of demand and supply factors

Credit Growth

Demand for Credit

(Inquiries)

Supply of Credit

(Originations)

Ability to Lend

(Liquidity)

Willingness to Lend

(Risk aversion)

Analyzed data pertaining

to previous crisis

Relationship between macro-

economic variables and

inquiries for key products

Money Supply in the economy

Changes in approval rates


The previous crisis represents an economic downturn scenario

that may help guide our direction during the current crisis

0%

2%

4%

6%

8%

10%

12%

14%

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008

Q32008

Q42008

Q12009

Q22009

Q32009

Q42009

Q12010

Q22010

Q32010

Q42010

YoY

Gro

wth

Rate

Growth in Real GDP



0

50

100

150

200

Q12007

Q32007

Q12008

Q32008

Q12009

Q32009

Q12010

Q32010

Q12011

Q32011

Q12012

Q32012

Indexed V

olu

mes

Growth in Inquiry and Origination Volumes

Inquiry Originations

Inquiry and origination volumes declined by almost 50% YoY

during the crisis period

Products considered: home loan, LAP, auto loan, personal loan and credit card


Index: Q1 2007 = 100


Demand for housing is closely associated with wealth creation

through the equity market

-60%

-30%

0%

30%

60%

90%

120%

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008

Q32008

Q42008

Q12009

Q22009

Q32009

Q42009

Q12010

Q22010

Q32010

Q42010

YoY

Gro

wth

Rate

Growth in Home Loan (HL) Inquiries and Share Price Index

HL Inquiries Share Price Index

Source: TransUnion CIBIL consumer database,

Oxford Economics

Correlation = 0.89

Share price index is the average value of BSE SENSEX


There is a linkage between overall industrial activity and

demand for loans against property (LAP)

-10%

-5%

0%

5%

10%

15%

20%

25%

-40%

-20%

0%

20%

40%

60%

80%

100%

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008

Q32008

Q42008

Q12009

Q22009

Q32009

Q42009

Q12010

Q22010

Q32010

Q42010

IIP Y

oY

Gro

wth

Rate

LA

P I

nquirie

s

YoY

Gro

wth

Rate

Growth in LAP Inquiries and Index of Industrial Production (IIP)

LAP Inquiries IIP


Oxford Economics

Correlation = 0.75

The index of industrial production measures the output of the industrial sector of the

economy, which includes manufacturing, utilities, mining and quarrying


Private consumption and demand for auto loans move together

2%

4%

6%

8%

10%

12%

-50%

0%

50%

100%

150%

200%

250%

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008

Q32008

Q42008

Q12009

Q22009

Q32009

Q42009

Q12010

Q22010

Q32010

Q42010

PC

YoY

Gro

wth

Rate

AL I

nquirie

s

YoY

Gro

wth

Rate

Growth in Auto Loan (AL) Inquiries and Private Consumption (PC)

AL Inquiries Private Consumption


Oxford Economics

Correlation = 0.78

Private Consumption is the value of goods and services consumed by households

and non-profit institutions serving households expressed in local currency


Household financial liabilities and demand for personal loans

are closely associated

10%

15%

20%

25%

30%

-200%

-100%

0%

100%

200%

300%

400%

500%

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008

Q32008

Q42008

Q12009

Q22009

Q32009

Q42009

Q12010

Q22010

Q32010

Q42010

HF

L Y

oY

Gro

wth

Rate

PL I

nquirie

s

YoY

Gro

wth

Rate

Growth in Personal Loan (PL) Inquiries and Household Financial Liabilities (HFL)

PL Inquiries Household financial libilities


Oxford Economics

Correlation = 0.97

Household financial liabilities is defined as the combined liabilities of all people

in a household. It includes loans and borrowings from banks, housing finance

companies (HFCs) and nonbanking financial corporations (NBFCs).


Demand for credit cards, being a lifestyle payment product, is

connected with household wealth

12%

14%

16%

18%

20%

-100%

-50%

0%

50%

100%

150%

200%

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008

Q32008

Q42008

Q12009

Q22009

Q32009

Q42009

Q12010

Q22010

Q32010

Q42010

GH

W Y

oY

Gro

wth

Rate

CC

Inquirie

s

YoY

Gro

wth

Rate

Growth in Credit Card (CC) Inquiries and Gross Household Wealth (GHW)

CC Inquiries Gross Household wealth


Oxford Economics

Correlation = 0.87

Gross household wealth represents the total value of assets (financial

as well as non-financial) minus the total value of outstanding liabilities

of households (including non-profit institutions serving households)


The ability of financial institutions to lend can be determined

by money supply (M2) in the economy

0%

5%

10%

15%

20%

25%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008

Q32008

Q42008

Q12009

Q22009

Q32009

Q42009

Q12010

Q22010

Q32010

Q42010

Money S

upply

(M2)

YoY

Gro

wth

Rate

Origin

ation B

ala

nces

YoY

Gro

wth

Rate

Growth in Origination Balances and Money Supply (M2)

Origination Balances Money Supply (M2)


Oxford Economics

Products considered: home loan, LAP, auto loan, personal loan and credit card

Correlation = 0.70

Money supply (M2) includes cash in circulation, current account deposits as well as all

time-related deposits, savings deposits, and non-institutional money-market funds


Approval rates declined for all key products during the crisis

period indicating increased risk aversion

0%

20%

40%

60%

Home Loan LAP Auto Loan Personal Loan Credit Card

Appro

val R

ate

%

Products

Approval Rates during Crisis Period

2007 Q2 to2008 Q1

2008 Q2 to2009 Q1

2009 Q2 to2010 Q1

-16%

-28%

-22%

-30%-11%



Secured lending products are expected to see more pronounced

decline in demand

ProductMacro

Variable

YoY 2020

Forecast

Outlook for

DemandKey Dynamics

Home

Loans

Share Price

Index-11.0%

• Reduction in affordability

• Postponement of home purchases

• Drop in home prices / attractive offers by builders

LAP IIP -2.9%

• Lower manufacturing / services output

• Drop in real estate prices

• Need of finance to revive business

Auto LoansPrivate

Consumption-1.7%

• Reduction in discretionary spending

• Impact on travel and tour business

• Diminished ability and need to travel

Personal

Loans

Household

Liabilities+15.1%

• Need of funds to bridge personal finance gap

• Flexible product structure

• Greater access via digital channels

Credit

Cards

Household

Wealth+9.6%

• Increase in the need for digital payments

• Reduction in discretionary spending

• Postponement of lifestyle purchases

Low High

Low High

Low High

Low High

Low High

[Oxford Economics]


Liquidity may not be a challenge consequent to rate cuts and

other fiscal measures initiated by the regulator

0%

5%

10%

15%

20%

25%

Q12018

Q22018

Q32018

Q42018

Q12019

Q22019

Q32019

Q42019

Q12020

Q22020

Q32020

Q42020

YoY

Gro

wth

Rate

Money Supply (M2)



Lenders are likely to tighten their credit policy and customer

selection norms to manage and mitigate risk

Product Willingness Key Dynamics

Home Loans• Backed by security, lower default probability

• Lower interest rates, reduced margins

LAP• Higher risk of default in smaller businesses

• Irregular cash flows may present assessment challenges

Auto Loans• Avoiding exposure to tour / travel segment

• Challenges in repossession and resale of vehicles

Personal Loans

• Unsecured in nature, increased risk of default

• Key product offering for many lenders especially FinTechs

• Higher margins, increased profitability

Credit Cards

• Revolving credit line which can be periodically managed

• Spending and behavior can be monitored

• Leveraging CASA / internal database for acquisition

Low High

Low High

Low High

Low High

Low High


• Demand for products like credit cards and personal loans will remain moderate as consumers

look to secure funds to bridge any personal finance gap

• Decline in discretionary spends and reduced affordability will impact demand for asset finance

products

• Given the inherent risk of products like LAP and personal loans, we anticipate a greater

decline in approval rates for these products

To summarize:


Asset Quality

What may be the likely impact on stress levels for major products?


Portfolio delinquency rates have remained largely steady in

the last three years, with the exception of LAP

0%

1%

2%

3%

4%

5%M

ar-

17

Ju

n-1

7

Sep-1

7

Dec-1

7

Mar-

18

Jun-1

8

Sep-1

8

Dec-1

8

Mar-

19

Jun-1

9

Sep-1

9

Dec-1

9

Fe

b-2

0

% B

ala

nce in 9

0+

DP

D

Balance-level 90+ Delinquency Rate by Product

LAP

Auto Loan

Home Loans

Credit Card

Personal Loan



Impact on asset quality can be determined by analyzing

consumer scores, collection roll rates and payment hierarchy

Asset Quality

Consumer Risk

Scores

Shifts in borrower risk tiers

across product portfolios and

delinquency rates associated

with each tier

Collection Roll

Rates

Number of accounts

becoming newly delinquent

and flowing into subsequent

delinquency buckets

Payment

Hierarchy

The order in which

consumers prioritize

payments during times of

financial hardship


We looked at 6-month risk tier movement for non delinquent

consumers and their delinquency rates thereof

Risk Tier (t+6)90+ Balance DPD rate

(t+6)

Product-level

BalanceSubprime

Above

Subprime Subprime

Above

Subprime

Ris

k T

ier

(t =

0) Subprime

No

upgradeUpgrade High Risk Low Risk

Above

SubprimeDowngrade

No

downgrade

Very High

Risk

Very Low

Risk

Risk tier movements and DPD

rates can be simulated to

gauge the likely impact on

delinquency rates Subprime segment constitute a CV score of <=680

and above subprime a CV score of >=681


Share of portfolio in very high risk segment has increased for

credit cards and personal loans in the last one year

2%

4%

6%

8%

Q22016

Q32016

Q42016

Q12017

Q22017

Q32017

Q42017

Q12018

Q22018

Q32018

Q42018

Q12019

Q22019

% o

f B

ala

nce

Share of Portfolio in Very High Risk Segment

Auto Loan

Credit Card

Personal Loan

LAP

Home Loans


Very High Risk segment refers to those consumers who have moved

from above subprime segment to subprime segment in next 6 months


During the same time period, share of portfolio in low risk

segment has decreased for credit cards

1%

2%

3%

4%

5%

Q22016

Q32016

Q42016

Q12017

Q22017

Q32017

Q42017

Q12018

Q22018

Q32018

Q42018

Q12019

Q22019

% o

f B

ala

nce

Share of Portfolio in Low Risk Segment

Auto Loan

Personal Loan

LAP

Credit Card

Home Loans


Low Risk segment refers to those consumers who have moved from

subprime segment to above subprime segment in next 6 months


Delinquency rate in very high risk segment has moved up for

home loans and LAP in the last one year

0%

4%

8%

12%

16%

Q22016

Q32016

Q42016

Q12017

Q22017

Q32017

Q42017

Q12018

Q22018

Q32018

Q42018

Q12019

Q22019

% B

ala

nce in 9

0+

DP

D

Delinquency Rate for Very High Risk Segment

Credit Card

LAP

Personal Loan

Home Loans

Auto Loan


Very High Risk segment refers to those consumers who have moved

from above subprime segment to subprime segment in next 6 months


Analyzing bucket net flow rates and lagged flow to 90+ would

also help gauge the impact on 90+ delinquency rate

BucketOutstanding Balance (value) Net Flow Rates

T T+1 T+2 T+3 T+4 T+1 T+2 T+3 T+4

Current 100.0 102.0 105.0 107.0 110.0

1-29 4.0 5.0 5.0 6.0 6.0 5% 5% 6% 6%

30-59 2.0 3.0 3.0 4.0 5.0 75% 60% 80% 83%

60-89 1.6 1.8 2.2 2.5 3.0 90% 74% 83% 75%

90+ 1.3 1.5 1.8 2.1 2.4 94% 100% 95% 96%

Total 108.9 113.3 117.0 121.6 126.4 Lagged flow to 90+ is the

product of diagonal bucket

net flow rates90+ DPD 1.90% 2.40%

[Illustration]

Impact on DPD can

be calculated basis

lagged flow to 90+

Bucket net

flow rates

can be

simulated to

get lagged

flow to 90+

Balance in next delinquency bucket

(eg: 30-59) on time T+1Bucket net

flow rate Balance in previous delinquency

bucket (eg: 1-29) on time T

=1

2

3

4


Delinquency rate and lagged flow to 90+ move in same direction

which enables us to use one to predict the other

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

Q12017

Q22017

Q32017

Q42017

Q12018

Q22018

Q32018

Q42018

Q12019

Q22019

Q32019

Q42019

Rate

(%

)

Home Loan and Personal Loan Delinquency and Lagged Flow Rate

HL lagged flow

HL 90+ delq

PL lagged flow

PL 90+ delq



Lagged flow to 90+ has deteriorated for LAP and credit cards

in the last one year

0%

1%

2%

3%

4%

5%

6%

Q12017

Q22017

Q32017

Q42017

Q12018

Q22018

Q32018

Q42018

Q12019

Q22019

Q32019

Q42019

Perc

enta

ge

Lagged Flow to 90+ DPD

LAP

Auto Loan

Credit Card

Home Loans

Personal Loan



We studied payment hierarchy for two separate product

combinations, which represent different consumer groups

Credit

card

Home

loan

Auto Credit

card

Consumer

durable loan

Personal

loan

Significantly

different

populations

Study 2Study 1

• More affluent

• Higher income

• Lower risk

• Tighter lending criteria

• Lower income

• Higher risk

Source: 2019 TransUnion CIBIL Payment Hierarchy Study


0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

Jan-1

4

Mar-

14

May-1

4

Jul-14

Sep-1

4

Nov-1

4

Jan-1

5

Mar-

15

May-1

5

Ju

l-1

5

Sep-1

5

Nov-1

5

Jan-1

6

Mar-

16

May-1

6

Jul-16

Sep-1

6

Nov-1

6

Jan-1

7

Mar-

17

May-1

7

Jul-17

Sep-1

7% A

ccounts

in 9

0+

DP

D

Study Cohorts

Account-level 90+ Delinquency Rate

Credit Cards

Auto Loans

Home Loans

The study on payment hierarchy revealed that home loans

generally have the highest payment priority


Study cohort is the month for which the sample of consumers holding the

above products was picked (~300K per cohort)


0.0%

0.5%

1.0%

1.5%

2.0%

Jun-1

4

Au

g-1

4

Oct-

14

Dec-1

4

Fe

b-1

5

Apr-

15

Jun-1

5

Aug-1

5

Oct-

15

Dec-1

5

Fe

b-1

6

Apr-

16

Ju

n-1

6

Aug-1

6

Oct-

16

Dec-1

6

Fe

b-1

7

Apr-

17

Jun-1

7

Aug-1

7

Oct-

17

Dec-1

7

Fe

b-1

8

% A

ccounts

in 9

0+

DP

D

Study Cohorts

Account-level 90+ Delinquency Rate

CreditCards

ConsumerDurableLoans

PersonalLoans

Amongst unsecured lending products, personal loans generally

have the highest payment priority


Study cohort is the month for which the sample of consumers holding the

above products was picked (~300K per cohort)


We simulated these risk related factors to determine the likely

impact on asset quality

Risk FactorsHistoric

Ranges

Base

case

Worst

Case

Increase in share of portfolio moving from above

subprime to subprime (Very High Risk Segment)0.5% - 0.8% +1% +2%

Increase in delinquency rate of Very High Risk

Segment 1.04X - 1.07X 1.1X 1.2X

Increase in share of subprime portfolio remaining in

same risk tier (High Risk segment)0.3% - 0.6% +1% +2%

Increase in delinquency rate of High Risk Segment 1.03X - 1.06X 1.1X 1.2X

Deterioration in net flow rates for all delinquency

buckets4% - 7% 10% 20%

Above simulations carried out individually for home loan, LAP, auto loan, personal loan and credit card


Asset quality for unsecured products is likely to be impacted

more severely than asset backed products

Product Impact Key Dynamics

Home Loans

• Adverse impact on consumers financial situation

• Possibility of non-payment for under-construction home loans

• Highest payment priority

LAP

• Shutdown of businesses / Slowdown of orders

• Irregular cash flows / poor churning

• Emergency credit line / sub-ordinate debt to small businesses

Auto Loans• Slowdown of cab services and car rental businesses

• Migration of drivers to their hometown

Personal Loans

• Job losses / Lay-offs / Pay-cuts

• Recent acquisition by NBFCs / FinTechs from high risk customers

• Increase in loan stacking behavior

Credit Cards

• Job losses / Lay-offs / Pay-cuts

• Least payment priority

• Alternate and convenient payment option

Low High

Low High

Low High

Low High

Low High


The impact on individual lender’s portfolio will also depend on

the risk management practices adopted by that lender

Origination growth

Slower pace of growth may lead to

increase in delinquency levels –

“denominator effect”Profile of existing consumers

Acquisition of high risk consumers

in past, age profile, income, etc.

Current portfolio mix

Open market acquisitions, sourcing

from DSA, collateral coverage

Credit models

Use of risk models and data

analytics in credit underwriting

Portfolio monitoring

Use of behavior scorecards and

early warning systems

Collection practices

Collection prioritization models and

cohesive treatment strategies

Asset Quality


• Ascertaining the impact of COVID-19 on asset quality is a complex picture dependent on

number of interlocking factors like consumer credit scores, collection roll rates and payment

hierarchy

• A wider analysis of these factors predicts that asset quality will likely be impacted most for

personal loans and credit cards with home loans and auto loans experiencing less of a shift

• In these difficult times, lenders need to actively monitor their portfolio and implement analytics

driven risk and collection management practices to minimize impact of any potential risk

To summarize:


Key implications from findings for lenders to consider

Future Readiness Lending Strategy Infra Risk Management

• Innovate and redesign

distribution channels

• Reconsider the customer

management framework

• Facilitate seamless customer

onboarding

• Digitize and automate internal

operations

• Decide on the choice of

customers (open market /

existing)

• Evaluate partnership models

(co-lending / co-origination)

• Leverage on digital sourcing

channels

• Decide on the right product

mix (secured versus

unsecured)

• Use of risk models and data

analytics in credit underwriting

• Segment customers basis

their credit behavior

• Monitor portfolio using

behavior scorecards and early

earning tools

• Implement collection

prioritization models to

maximize recoveries

v

vv ThankThank You!


Disclaimer

This Presentation is prepared by TransUnion CIBIL Limited (TU CIBIL). This Presentation is based on

collation of information, substantially, provided by credit institutions who are members with TU CIBIL.

While TU CIBIL takes reasonable care in preparing the Presentation , TU CIBIL shall not be responsible

for errors and/or omissions caused by inaccurate or inadequate information submitted to it by credit

institutions. Further, TU CIBIL does not guarantee the adequacy or completeness of the information in

the Presentation and/or its suitability for any specific purpose nor is TU CIBIL responsible for any access

or reliance on the Presentation and that TU CIBIL expressly disclaims all such liability. This Presentation

is not a recommendation for rejection / denial or acceptance of any application nor any recommendation

by TU CIBIL to (i) lend or not to lend; (ii) enter into or not to enter into any financial transaction with the

concerned individual/entity. The user should carry out all the necessary analysis that is prudent in its

opinion before making any decisions based on the Information contained in this Presentation. The use of

the Presentation is governed by the provisions of the Credit Information Companies (Regulation) Act,

2005, the Credit Information Companies Regulations, 2006, Credit Information Companies Rules, 2006.

No part of this presentation should be copied, circulated, published without prior approvals.

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