Credit Scoring
October 2008
Thomas J. Quinn, VP - Global Scoring Solutions
Fair Isaac Corporation
Confidential. The material in this presentation is the property of Fair Isaac Corporation, is provided for the recipient only, and shall not be used, reproduced,
or disclosed without Fair Isaac Corporation's express consent.
© 2008 Fair Isaac Corporation.
Dynamic times for financial
services
• U.S. mortgage lending situation has blossomed into an
unprecedented world-wide credit crisis
– Lack of liquidity
– In many markets -- less consumer demand for credit and lenders
have tightened credit criteria
• Lenders are questioning validity of scoring tools given
increasing losses and credit quality deterioration
• Lender currently focused on risk mitigation, but …
• Also want to “be ready” for when the pendulum swings
back to more robust economic conditions
Opportunities exist to help our
client base through this cycle!
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Discussion topics
• Revisiting the basics
• Credit scoring usage opportunities
• Credit scoring innovation
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Scores are designed to rank
order
680
660
640
620
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Credit scoring basics
• Credit score distributions are not static, rather they are
fluid and it is expected that they will change over time
• It is natural that score alignment will move and change
over time
– Changes in data reported, consumer credit behaviors, lender
practices, changing economic conditions, score updates, etc.
• For these reasons, it is important that each lender
monitor and track their portfolio dynamics by
credit score on a frequent basis and make
adjustments to strategies as needed
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
FICO® score odds alignment
U.S. example
All Existing & New Accounts
Trade line Performance
Bad Definition: 90+ days & Charge-offs
Odds (Bads Vs Non-Bads)
1,000
100
10
1
500
520
540
560
580
600
620
640
660
680
700
720
®
FICO Score
2000 - 2002
2003 - 2005
2005 - 2007
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
740
760
780
800
Industry odds by “Lender”
100.0
10.0
1.0
<5
50 00
05
52 19
05
54 39
05
56 59
05
58 79
05
60 99
06
62 19
06
64 39
06
66 59
06
68 79
06
70 99
07
72 19
07
74 39
07
76 59
07
78 79
079
9
80
0+
Odds (90+ dpd)
1000.0
FICO® Score
Industry
Lender B in region 3
Lender C in region 1
Lender D in region 2
Lender A in all regions
Lender B in region 1
Lender C in region 2
Lender E in all regions
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Opportunities
• Education & training
– Create educational programs that educate your clients,
regulatory bodies, etc., on how your value-added tools are
developed, what they are designed to do, and the end-user
responsibilities when using credit scores
• Tracking & reporting services
– Create quarterly or monthly credit scoring tracking report that
provides trending on score distribution, key credit variables, and
score performance for broad-based population, as well as by
industries of interest
• Client-specific validation services
– Create service to customized tracking and trending
reports for client on their portfolios
• More frequent update/redevelopments
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Discussion topics
• Revisiting the basics
• Credit scoring usage opportunities
• Credit scoring innovation
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Early 2000s: Focus on growth
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Today: Focus on controlling risk
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Opportunities
• Increasingly, lenders are considering use of multiple
scores to reduce risk exposure
– Multiple scores predicting different types of risk (general risk vs.
bankruptcy risk, for example)
– Pulling of credit scores from “secondary” credit bureaus in
markets where multiple bureaus exist
• Frequent credit score “refreshes/updates” on existing
customers is more prevalent
• Benefits
– Lender – better decisions/more profitable portfolios
– Bureau – sell more information/services &
deepen relationship
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Opportunity: dual score approach
• Lender uses multiple scores to identify “swap set
segments” where alternative decision/treatment is
employed
New
Bankruptcy
Cutoff
High
OVERALL RISK
Previous
Risk Score
Cutoff
Approvals
Low
High
CB BANKRUPTCY RISK
Previously Approve – now Decline
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Low
Case study
Dual score use - new account bookings
Objective
Reduce loss reserves & improve
rating position
Population
New account approvals
Tactic
Identify more appropriate approvals to modify
applicant screening segmentation
Methodology
Explore retrospective results to design initial
challenger tests to evaluate new strategies
incorporating different tools
Tools
Risk compared to Risk & Bankruptcy Score
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Benefit analysis
Champion
Challenger 1
Difference
Percent of
Approvals
72.5%
71.1%
-1.4%
-1.9%
Net Revenue
(000)
$10,690
$10,420
-$270
-2.5%
Bankruptcy
Losses (000)
$1,700
$1,530
-$170
-10.0%
Total Losses
(000)
$3,100
$2,850
-$250
-8.1%
Bad Rate
2.86%
2.75%
-0.11%
-3.8%
Reduced bankruptcy losses by 10% with minimal reduction in revenue
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Conceptual value of
“secondary” score
• In many countries, there are multiple credit bureaus in
operation
• Often, certain data elements captured on the consumer
can be different across bureaus—driving differences in
scores
• Accessing scores from multiple bureaus provides the
lender with additional information for credit granting
decisions
– Swap In segments
– Swap Out segments
• Question: understanding & quantifying the
cost/benefit trade-off
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Case study results
•
•
•
Large U.S. lender (credit card portfolio)
Champion: Custom score and FICO® score from one bureau
Challenger: Custom score and FICO® score from primary &
secondary bureaus
– Identify swap sets where use of secondary bureau FICO® score would
change approve/decline decision. Assess volume impacts as well as
resulting loss performance impacts
•
Test results
– ~5% increase in approvals with no increase in risk
– Devised strategy to obtain secondary score on targeted segments
– Exceeded client’s internal ROI threshold (value provided exceeded
incremental bureau costs and operational efforts to implement)
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Score refresh – how often is
enough?
• Obtaining updated credit bureau scores on a
portfolio facilitates a lender’s ability to more
accurately understand the over-risk & opportunity
value of a given customer.
– Provides a broad view of their credit practices not
captured with internal behavioral scores and metrics
• How often do credit scores migrate over time? How
often should lenders refresh or obtain updated
scores?
• How valuable is a refreshed score in
predicting delinquent behavior?
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
FICO® score migration
3-, 6-, and 9-Month Migrations
Jun 07 - Mar 08
3 months
6 months
75%
80%
65%
58%
70%
% of accounts
9 months
60%
50%
40%
30%
20%
10%
6%
9%11%
8%10%10%
7%
10%12%
4%
7% 9%
0%
Low to -41
-40 to -21
-20 to +20
+21 to +40
Score difference*
* Positive score difference equates to score increase over time
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
+41 to High
How much more predictive is a
fresher score?
Swap set table - 14-Month Good/Bad Odds
FICO® Score Migration: Through March 07
Beginning of
Migration
Score Cutoff % Above % Below
End of Migration
Swap Set odds
(10-point buffer)
(10-point buffer)
% Migrated
Above
% Migrated
Below
Migrated
Above
Migrated
Below
3-month 690
81.4%
18.6%
1.5%
1.8%
46.7
14.9
6-month 690
81.4%
18.6%
2.3%
2.5%
53.0
12.8
9-month 690
81.2%
18.8%
3.1%
3.1%
53.7
11.8
3-month 730
70.3%
29.7%
1.8%
2.0%
58.3
34.3
6-month 730
70.1%
29.9%
2.8%
2.9%
64.9
26.9
9-month 730
69.8%
30.2%
3.6%
3.4%
75.7
23.9
•
•
The older scores exhibit larger swap sets and greater differences
between the actual risk of the swap sets. (The older scores are
further away from providing the correct risk assessment.)
Lenders using the older scores are at greater risk of making
suboptimal decisions on the swap set consumers.
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Discussion Topics
• Revisiting the basics
• Credit scoring usage opportunities
• Credit scoring innovation
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Lender “requests” for help
• Cracking the over-indebtedness question
• Mining alternative data for risk prediction
insights
• Forecasting future odds
• Modeling consumer responsibility
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Understanding debt
sensitivity
• Risk
– Most credit scores predict credit risk from bureau report snapshot
• Predicts future performance based on past behavior
– Incremental debt impact not considered
– Same score implies same risk
– However, same score includes individuals within different credit profiles
• Capacity
– Identifying consumers’ ability to safely manage incremental debt
• Not known at the time of scoring
– Different credit profiles suggest different incremental debt sensitivity
• Low capacity = higher risk sensitivity to incremental debt
• High capacity = lower risk sensitivity to incremental debt
– Must be considered relative to risk
– Income not always a proxy for debt sensitivity
• Improved understanding of debt sensitivity within
risk levels can drive higher profits
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Strong risk separation as
balance increases
New Bankcards - Mid-Low FICO® Score
FICO® 660-699 by CCI
Low
Med
High
Pop%
30%
45%
Bad Rate
25%
20%
37%
15%
18%
10%
5%
Revolving Balance Change
Results on Pooled Bankcard sample
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
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h
H
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Mining alternative data
• Where alternative data exists
– Develop scoring solutions that incorporate alternative data not
being captured via traditional infrastructure
• Align to existing solutions in place
• Create “seamless” interface for client access
• Ensure regulatory compliant
• Where alternative data not readily available
– In some markets, opportunity to build risk, income, asset
“indexes” based on key population segments (postal code, etc.)
– Infer that “no hit/no score” population is likely to have similar risk
and income profiles to the segment
– Use indexes (along with layering of other elements)
for credit granting and line assignment decisions
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Focus on the future
• Cracking the over-indebtedness question
• Mining alternative data for risk prediction
insights
• Forecasting future odds
• Modeling consumer responsibility
In the research
phase
October 2008
© 2008 Fair Isaac Corporation. All Rights Reserved.
Thank You
Thomas J. Quinn
[email protected]
October 2008
Confidential. The material in this presentation is the property of Fair Isaac Corporation, is provided for the recipient only, and shall not be used, reproduced,
or disclosed without Fair Isaac Corporation's express consent.
© 2008 Fair Isaac Corporation.