Score migration – strategies for turbulent times - Experian · Regress VantageScore® against good bad flag with 1, 2, 3, 6 and 12 month change in score Least squares trending of

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Score migration –strategies for turbulent times

Chuck Robida, Experian

© 2010 Experian Information Solutions, Inc. All rights reserved.Experian Public. 2

Score migration

What is score migration?

► Shift in score over time for aggregate credit bureau population

► Drift in the interpretation of the score

► Decrease in performance between refreshes

► Change in scores of individual consumers

What should you be concerned about?


Average VantageScore® over time

730

735

740

745

750

755

760

200401

200404

200407

200410

200501

200504

200507

200510

200601

200604

200607

200610

200701

200704

200707

200710

200801

200804

200807

200810

200901

200904

200907


Average VantageScore® over timeBy product type


Bad rate (90+ DPD) of new accounts


Bad rate (90+ DPD) of existing accounts


Interpretation of score

There is not a fixed bad rate at a given score

Score interpretation varies

► Product type

► Lender to lender for the same product

► Environmental factors

● Unemployment

● Asset values


New real estate good bad odds


Existing real estate good bad odds


New bankcard good bad odds


Existing bankcard good bad odds


Average score issue


Score drift

Interpretation of scores drifts over time – including custom models

Portfolio risk may be increasing without any change in score distribution

A consistent strategy may not yield consist risk

Average score is not a robust measurement of portfolio risk

Actions

► Monitor, monitor, monitor to understand, adjust and execute

► Loss forecasting using macro economic variables

● Advanced tools for provisioning in a new age – Tuesday 9:45

► Use interval bad rate for averaging to account for exponential relationship of bad rate with score


Analyze refresh intervals

Organizations may increase time between score refreshes to cut costs

Assess impact on score performance

t0 t+12t-6 t-3 t-2 t-1

90+ DPDVantageScore®


Degradation of KS over time


Drop in bad capture rate in lowest scoring 10%


Refresh intervals

Performance of score begins to deteriorate immediately and linearly

Internal score will accurately assess risk of the some of the accounts

Greatest exposure is on accounts that appear to be low risk on internal scores, but whose is increasing on other accounts

Actions

► Evaluate cost / benefit analysis of update cycles

► Evaluate cost / benefit analysis of risk triggers


Does knowledge of score trend increase predictive power?

Regress VantageScore® against good bad flag with

► 1, 2, 3, 6 and 12 month change in score

► Least squares trending of 1, 2, 3, 6 and 12 scores, projecting score 1-12 months after outcome at consumer level

► Use exponential smoothing of score and repeat least squares trending analysis

● Smoothed_Scoret0 = Scoret0

● Smoothed_Scoret-1 = 0.9*Smoothed_Scoret0 + 0.1*Scoret-1

● Smoothed_Scoret-2 = 0.9*Smoothed_Scoret-1 + 0.1*Scoret-2

● Smoothed_Scoret-1 = 0.9*Smoothed_Scoret-2 + 0.1*Scoret-3


Score trending


Existing bankcard accounts


Existing bankcard accounts


Existing bankcard accountsGood bad odds


Value of score trending

None of the trending analysis methods resulted in any improvement in predictive power

Positive changes not indicative of lower risk

Current score is the best predictor

► Scores already incorporate time dependent variables such as delinquency and inquiries

► Score is a composite of many factors and is too high a level to accurately discriminate between trends and noise


How much do consumer scores change over time

Look at progression of VantageScores® after:

► One year (June 2004 – June 2005)

► Three years (June 2004 – June 2007)

► Five years (June 2004 – June 2009)


Consumer scores over timeOne year

501-550551-600

601-650651-700

701-750751-800

801-850851-900

901-950951-990

501-5

50551-6

00601-6

50651-7

00701-7

50751-8

00801-8

50851-9

00901-9

50951-9

90

6 / 2 0 0 76 / 2 0 0 4

65.0%-70.0%

60.0%-65.0%

55.0%-60.0%

50.0%-55.0%

45.0%-50.0%

40.0%-45.0%

35.0%-40.0%

30.0%-35.0%

25.0%-30.0%

20.0%-25.0%

15.0%-20.0%

10.0%-15.0%

5.0%-10.0%

0.0%-5.0%


Consumer scores over timeThree years

501-550551-600

601-650651-700

701-750751-800

801-850851-900

901-950951-990

501-5

50551-6

00601-6

50651-7

00701-7

50751-8

00801-8

50851-9

00901-9

50951-9

90

6 / 2 0 0 76 / 2 0 0 4

60.0%-65.0%

55.0%-60.0%

50.0%-55.0%

45.0%-50.0%

40.0%-45.0%

35.0%-40.0%

30.0%-35.0%

25.0%-30.0%

20.0%-25.0%

15.0%-20.0%

10.0%-15.0%

5.0%-10.0%

0.0%-5.0%


Consumer scores over timeFive years

501-550551-600

601-650651-700

701-750751-800

801-850851-900

901-950951-990

501-5

50551-6

00601-6

50651-7

00701-7

50751-8

00801-8

50851-9

00901-9

50951-9

90

6 / 2 0 0 96 / 2 0 0 4

50.0%-55.0%

45.0%-50.0%

40.0%-45.0%

35.0%-40.0%

30.0%-35.0%

25.0%-30.0%

20.0%-25.0%

15.0%-20.0%

10.0%-15.0%

5.0%-10.0%

0.0%-5.0%


Consumer scores over time

Consumer scores are relatively stable over time

Consumer behavior not dynamic –although unfortunate events can happen to any consumer

Unlikely to ‘rehabilitate’ low scoring consumers, especially in short term (one to two years)


Conclusions

A score is not an absolute predictor of bad rate

► Portfolio risk may increase with out a change in score distribution

► Average scores are not a good indicator of portfolio risk

Score interpretation drifts over time

► Portfolio risk can rise with no change in score distribution or strategy

► Monitoring is essential

Predictive power of scores begins to deteriorate immediately and linearly

► Need to assess cost benefit of refresh cycles

► Look to real time updates

Trends in scores do not add lift in predictive power

Consumers’ behavior is relatively consistent over time


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Score migration – strategies for turbulent times - Experian · Regress VantageScore® against good bad flag with 1, 2, 3, 6 and 12 month change in score Least squares trending of