Score migration strategies for turbulent times Chuck Robida, Experian Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc. Other product and company names mentioned herein may be the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian Information Solutions, Inc.
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? 2
Average VantageScore over time 760 755 750 745 740 735 730 200907 200904 200901 200810 200807 200804 200801 200710 200707 200704 200701 200610 200607 200604 200601 200510 200507 200504 200501 200410 200407 200404 200401 3
Average VantageScore over time By product type 4
Bad rate (90+ DPD) of new accounts 5
Bad rate (90+ DPD) of existing accounts 6
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 7
New real estate good bad odds 8
Existing real estate good bad odds 9
New bankcard good bad odds 10
Existing bankcard good bad odds 11
Average score issue 12
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 13
Analyze refresh intervals Organizations may increase time between score refreshes to cut costs Assess impact on score performance VantageScore 90+ DPD t -6 t -3 t -2 t -1 t 0 t +12 14
Degradation of KS over time 15
Drop in bad capture rate in lowest scoring 10% 16
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 17
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_Score t0 = Score t0 Smoothed_Score t-1 = 0.9*Smoothed_Score t0 + 0.1*Score t-1 Smoothed_Score t-2 = 0.9*Smoothed_Score t-1 + 0.1*Score t-2 Smoothed_Score t-1 = 0.9*Smoothed_Score t-2 + 0.1*Score t-3 18
Score trending 19
Existing bankcard accounts 20
Existing bankcard accounts 21
Existing bankcard accounts Good bad odds 22
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 23
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) 24
Consumer scores over time One year 65.0%-70.0% 60.0%-65.0% 55.0%-60.0% 50.0%-55.0% 45.0%-50.0% 951-990 901-950 851-900 951-990 901-950 851-900 801-850 751-800 701-750 651-700 601-650 551-600 501-550 801-850 751-800 701-750 651-700 601-650 551-600 501-550 6/ 2004 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% 6/ 2007 25
Consumer scores over time Three years 60.0%-65.0% 55.0%-60.0% 50.0%-55.0% 45.0%-50.0% 40.0%-45.0% 951-990 901-950 851-900 951-990 901-950 851-900 801-850 751-800 701-750 651-700 601-650 551-600 501-550 801-850 751-800 701-750 651-700 601-650 551-600 501-550 6/ 2004 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% 6/ 2007 26
Consumer scores over time Five years 50.0%-55.0% 45.0%-50.0% 40.0%-45.0% 35.0%-40.0% 951-990 901-950 851-900 951-990 901-950 851-900 801-850 751-800 701-750 651-700 601-650 551-600 501-550 801-850 751-800 701-750 651-700 601-650 551-600 501-550 6/ 2004 6/ 2009 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% 27
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) 28
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 29
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For additional information, please contact: Chuck.Robida@experian.com