Vision 2014: How-does-risk-change-when-payment-and-spend-behavior-changes

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How does risk change when payment and spend behavior changes?

Michael McGinley Experian

#vision2014

2 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.

Using trended data to understand consumer behavior

Payment data

► Payments and risk

► Spend and risk

Creating a trended payment metric

Optimizing line and loan amounts

Agenda


Trended data


Trended data Experian retains 24 months of history on key data fields on every trade

Trade #1 Bank name Account # Open date Credit line Balance Actual payment Loan type

Visa 4812 3/9/1990 $25,800 $13,300 $650 Credit card

Account condition

Account status

Special comment

Months reporting

Minimum payment due

Months since update

Date of payment

OPEN CURR ACCT CH 231 $275 1 Aug-13

Trade #1 Balance Credit limit Minimum payment

Actual payment

Date of payment

Month 0 $13,300 $25,800 $275 $1,000 Aug

Month 1 $14,680 $25,800 $280 $1,000 July

Month 2 $16,060 $25,800 $286 $1,000 June

Month 3 $17,440 $25,800 $297 $1,000 May

Month 4 $18,820 $25,800 $310 $1,000 April

Month 5 $20,200 $25,800 $330 $1,000 March

Month 6 $21,580 $25,800 $343 $1,000 February

Month 7 $22,960 $25,800 $350 $1,000 January

These historical fields allow Experian the ability to:

Compare actual payments vs. the minimum payment due

or Compare actual

payments vs. the balance


A new view of the consumer

6 month yield:

$74

Average effective APR

16.99%

In the market?

Bankcard

BT Index (0-999)

875 BTs in last 12

months

2

Revolving trades:

4

Transacting trades:

0

Risk Index rating:

Unstable

Utilization in last 6 months:

+ 35%

Credit card bals last 6 months:

+ $8,000

Behavior

Risk Revenue

Response

Trended data allows Experian to reveal a comprehensive view of a consumer’s credit behavior to improve risk, response and profit…

Annual card spend:

$7,500

Payment behavior:

Min pay – 2%


Payment stress


Understanding payment behavior has always been a key metric for risk and line decisions within a portfolio

An “off us” view is now available to provide that same level of insight and value

Payment stress Viewing payment data, both currently and over time, to understand consumer risk

May 2014

VantageScore® 800

Balance $22,000

Status Good standing

Balances Min pay DUE Actual pay

Apr $22,000 $550 $2,000

Mar $26,000 $650 $3,500

Feb $20,000 $500 $12,000

Jan $28,000 $700 $8,000

Dec $35,000 $875 $6,000

Nov $45,000 $1,125 $11,000

Total $176,000 $ 42,000


Apr $22,000 $550 $550

Mar $19,000 $475 $475

Feb $16,000 $400 $400

Jan $15,000 $375 $400

Dec $11,500 $288 $320

Nov $9,000 $225 $300

$92,500 $ 2,645

Consumer A Consumer B


Experian stores historical payments, balances and requested payment data at the trade level

The data enables a detailed view of changes in pay rates and debt burdens that are not captured in credit scores, providing a unique profile of a consumer


May 2014

VantageScore® 800

Balance $22,000



Apr $22,000 $550 $2,000

Mar $26,000 $650 $3,500

Feb $20,000 $500 $12,000

Jan $28,000 $700 $8,000

Dec $35,000 $875 $6,000

Nov $45,000 $1,125 $11,000

Consumer A Paying well over minimum

payments

Demonstrated ability to pay

No stress



May 2014

VantageScore® 800

Balance $22,000



Apr $22,000 $550 $550

Mar $19,000 $475 $475

Feb $16,000 $400 $400

Jan $15,000 $375 $400

Dec $11,500 $288 $320

Nov $9,000 $225 $300

Consumer B

Making minimum payments for the last three months

Payment amounts vs. minimum due is decreasing over time

Payment stress

Experian stores historical payments, balances and requested payment data at the trade level

The data enables a detailed view of changes in pay rates and debt burdens that are not captured in credit scores, providing a unique profile of a consumer


Spend and risk


Risk flattens after >$5 thousand in spend while income continues to increase

Experian Total Annual Plastic Spend (TAPS)SM Bin

Spend is orthogonal to risk


Spend opportunity is correlated with credit risk

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

501-600 601-700 701-800 801-900 901-990

Low spend (<$25k/year, 100%=$659B)

Medium spend ($25k-65k/year, 100%=$653B)

High spend ($65k+/year, 100%=$653B)


Low spenders making minimum payments have elevated risk levels

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Bads are concentrated in

Low pay rates and

Low spend

Spend

$100K+

$50K–$100K

$25K–$50K

$10K–$25K

$5K–$10K

$1–$5K

Goods are concentrated in

High spend with high pay rate and

Low spend with higher pay rate


Payments analysis


Created a metric to capture aggregate six month credit card payments vs. the balances on those trades

Creating a payment metric


Apr $22,000 $550 $2,000

Mar $26,000 $650 $3,500

Feb $20,000 $500 $12,000

Jan $28,000 $700 $8,000

Dec $35,000 $875 $6,000

Nov $45,000 $1,125 $11,000


Apr $22,000 $550 $550

Mar $19,000 $475 $475

Feb $16,000 $400 $400

Jan $15,000 $375 $400

Dec $11,500 $288 $320

Nov $9,000 $225 $300

23% pay rate 2.8% pay rate

A single month is ineffective to capture meaningful behavior


Analysis population

Percentage of payments vs. balances

on credit cards over last six months

16 million random sample

6.4 million Calculated pay rate

metric

1.4 million $0 balances

7.8 million Historical balance data

3.2 million No historical credit card

trades

11 million VantageScore® scorable

5 million No hits or

unscorable


Pay rate discriminates risk within VantageScore® bands

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

Pay rate

90 D

PD

on

an

y t

rad

e

VantageScore® 650–700

0%–2.3%

2.3%–4.2%

4.2%–8.9%

8.9%–20.1%

20.1%–40.2%

40.2%–66.3%

66.3%–86.5%

86.5%–100%

> 100%

Min payers

Transactors

Pay rate

Lower pay rates have 3x more incidence of 90DPD than

higher pay rate segments over the next 18 months


650

660

670

680

690

700

Vantage


0%–2.3%

2.3%–4.2%

4.2%–8.9%

8.9%–20.1%

20.1%–40.2%

40.2%–66.3%

66.3%–86.5%

86.5%–100%

> 100%

The difference in performance is not detected in traditional credit scores

Min payers Transactors

Pay rate


0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

Pay rate

90 D

PD

on

an

y t

rad

e

701–750 VantageScore®

0%–2.3%

2.3%–4.2%

4.2%–8.9%

8.9%–20.1%

20.1%–40.2%

40.2%–66.3%

66.3%–86.5%

86.5%–100%

> 100%

Pay rate discriminates risk within VantageScore® bands

Pay rate


700

710

720

730

740

750

Vantage


0%–2.3%

2.3%–4.2%

4.2%–8.9%

8.9%–20.1%

20.1%–40.2%

40.2%–66.3%

66.3%–86.5%

86.5%–100%

> 100%

VantageScore® does not differentiate pay rates

Min payers Transactors

Pay rate


14.3%

12.5% 11.4%

9.5% 9.5%

7.4% 6.4%

4.8% 3.9%

90

DP

D o

n a

ny t

rad

e


Lower VantageScore® segments with high pay rates have better performance than many segments with higher VantageScore®

9.6% 9.1%

7.4%

6.1% 5.2%

4.1% 3.3%

2.5% 2.0%


0%–2.3% 2.3%–4.2% 4.2%–8.9%

8.9%–20.1% 20.1%–40.2% 40.2%–66.3%

66.3%–86.5% 86.5%–100% > 100%

6.5% 6.2%

4.9%

3.7% 3.2%

2.5% 1.9%

1.4% 1.2%

90

DP

D o

n a

ny t

rad

e



6.8 million consumers would swap positions within VantageScore® segments based on pay rates

0.8

1.8

1.6

1.1

0.8

0.4

0.2

0.4 0.3

Co

ns

um

ers

(m

m)


0.9

2.0

1.7

1.3

1.0

0.6 0.4

0.8 0.6


0%–2.3% 2.3%–4.2% 4.2%–8.9%

8.9%–20.1% 20.1%–40.2% 40.2%–66.3%

66.3%–86.5% 86.5%–100% > 100%

0.8

1.5 1.4

1.2

1.0

0.7

0.5

1.2

0.9

Co

ns

um

ers

(m

m)



4.5 million consumers swap out at a 2% 90DPD threshold on mortgage trades

4.1 3.3% 3.4%

3.1% 2.7%

3.0% 2.8%

2.0% 1.9%

Volume

90

DP

D o

n m

ort

ga

ge


2.7% 2.9%

2.7%

2.3% 2.2% 2.0%

1.8% 1.6%

1.4%

Volume

90

DP

D o

n m

ort

ga

ge


2.1% 2.0%

1.7%

1.4% 1.3% 1.3%

0.8% 0.9% 0.7%


0% - 2.3% 2.3% - 4.2% 4.2% - 8.9%

8.9% - 20.1% 20.1% - 40.2% 40.2% - 66.3%

66.3% - 86.5% 86.5% - 100% > 100%


8 million prospects would swap positions at a 2.95% delinquency threshold on credit cards

10.3%

8.5% 7.3%

5.8% 4.9%

4.1% 3.5%

2.4% 1.7%

Volume


6.7%

5.8%

4.3%

3.4% 2.9%

2.2% 1.6%

1.1% 0.9%

Volume


4.3% 4.0%

2.8%

2.0%

1.5% 1.1%

0.9% 0.7%

0.5%

Volume

751–800 VantageScore® 2.8% 2.7%

1.8%

1.2%

0.8% 0.6%

0.4% 0.4% 0.3%

Volume



Identifying risk before scores change

Payment behavior will serve as a leading

indicator of risk – identifying accounts that will

go delinquent before utilization spikes, before score

changes and before a consumer goes delinquent

Delinquency Score

decrease High

utilization Payment behavior


Optimizing line to

capture spend and

limit risk


Payments – the value proposition:

5%–10% of the population shifts VantageScore® segments based on payments:

► Acquire same number of consumers and have lower loss rates

► Acquire more consumers and have the same loss rates

► Improve $$ loss rates by shifting line to high spending, higher pay rate consumers

Understanding payment behavior provides an opportunity to substantially improve loss rates


Line optimization example:

Average line at 850 VantageScore® is $15,000

► Loss rates are .2%

Identify same number of accounts at .1 loss rates

► Increase lines to $20,000–$25,000

Identify segments in the 850 VantageScore® segment who would have losses greater than .2%

► Shift line down to $5,000–$10,000

Understanding payment behavior provides an opportunity to substantially improve loss rates


0

100

200

300

400

500

<$5K $5K–$10K $10K–$25K $25K–$50K $50K–$100K > $100K

Acco

un

t valu

e

Annual spend

Interest Fees Interchange

Optimizing line within the right segments of spend and risk will improve profitability

Higher spending consumers contribute more profit from both interchange and interest revenue

Profit contribution and VantageScore® by spend bin

Credit Limits > $20K

30% less

profit

807 796 789 774 785 762


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