making better decisions about your customers with next ... are the underlying criteria used in...

Luz Torrez Senior Director,

Experian Decision Analytics

© 2011 Experian Information Solutions, Inc. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc. Other product and company names mentioned herein are 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.Experian Public.

Making better decisions about your customers with next generation attributes, custom scoring and analytics

Linda HaranSenior Director,

Experian Decision Analytics

© 2011 Experian Information Solutions, Inc. All rights reserved. 2

Agenda

Attribute overview

Dual-bureau leveling process

Custom models

Improved decisions

Summary

Q & A


Definition of an attribute

Definition…

► Merriam-Webster – “an inherent characteristic”

► Decision Sciences – “a measurement of consumer behavior”

Result…

► Convert data into decisions

Data Knowledge Decision


Types of attributes

Type of creditRevolvingMortgage

Depth of creditTime on fileAge of oldest# of tradesInquiries

Utilization of creditUtilization %BalancesCredit limits

Payment performanceSatisfactory ratingsMinor delinquencyMajor derogatoryPublic record

Composition of credit


Canadian Premier AttributesSM

Includes dual-bureau attributes based on current industry and data reporting trends

Predefined attribute sets including a core set applicable for all analytical purposes and industry-specific sets that include the essential mix needed for optimal analytical results


Agenda

Attribute overview


Custom models

Improved decisions

Summary

Q & A


Changing times, call for re-evaluation of data

Shifts in reporting

► Finance industry and bankcard issuers have changed business model

► Emergence of private label partnerships with financial institutions

New data elements available

► Bureaus add new codes and fields

► New product designations

Changes in consumer behavior require additional dimensions

► Strategic mortgage default vs. stressed consumers

► Collections / curing

► Additional consumer disclosure now desired

While an existing set of attributes may continue to be predictive, changes in our industry and consumer behavior require new ways of evaluating consumer behavior


What you see, is not always what you get

Attribute descriptions may read the same, however, behind the scene definitions can vary substantially

► Disparate levels of dual-bureau knowledge across vendors or organizations

● Raw bureau data is complex

● Incomplete or outdated documentation

Properly leveling attributes will allow the same consumer with the same credit bureau data across the two bureaus to get the same score

► One set of strategies and / or models for all consumers

► Consistency in decisions

Credit bureaus translate the same data differently, making it difficult for lending institutions to interpret that data consistently, therefore the importance for dual-bureau perspective in attribute creation is key


Performance improvement talks

Statistics for head-to-head comparisons

► KS, Gini, worst-scoring capture rate, odds ratio, etc.

► Establishing standard processes to measure value from different outcomes in an automated and consistent format

Financial savings by identifying the right segments

► Risk – better identification of “bad” accounts minimizes losses

► Marketing – enhanced targeting maximizes campaign dollar spend

► Collections – identification of recoverable accounts leads to more recovered dollars at less expense

► Fraud – minimize manual review time and costs

Evaluating accuracy and efficiency of attributes is hard to measure, some of the methods used to evaluate performance:


Why the data is different

Each bureau has its own “black box” in which transformations happen

Metro 2 formatBureau transformations


General process for defining filters / attributes

Take a look at the available data elements and how they are populated

► Frequencies of fields

Determine a “sensible” definition of the attribute

Look at attribute frequencies across the bureaus

Where possible – look at profile examples where there are discrepancies across bureaus

Refine the definition and look at more frequencies and examples

View the data

Refine thedefinition


Equifax TransUnionBB All BanksBX Banks-CollectionFC Credit unionsFS Savings and loan co.ON National Credit Card Cos.

BB BankBC Credit card, Visa, M/C (issued by bank or trust co.)BR Royal BankBZ Bank/finance/trust credit cardFC Credit UnionFS Savings & Loans AssociationsON National credit card companiesQC Credit card issue by credit unions or non-personal finance companiesQU Credit Unions

Kind of business / industry codesRevolving bankcard category


Leveling example Revolving bankcard

Create an attribute that counts number of revolving bankcard trades

► Equifax

● Kind of business codes and narrative codes

► TransUnion

● Kind of business codes and narrative codes

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

0 1 to 2 3 to 4 5 to 6 7 to 8 9 to 10 > 10

BCC0300 Total Number of Revolving Bankcard Trades

Equifax Trans Union


Leveling example Revolving bankcard

Revolving bankcard definition

Includes lines of credit <$25,000; excludes HELOC

Refine definition

► Exclude narratives for “Credit Line Closed” from definition for Equifax. These are not bankcard specific.

► Include additional code for line of credit for Trans Union: industry code “BY” “Line of credit” designated for the inquiry segment, but has counts in the trade segment

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

0 1 to 2 3 to 4 5 to 6 7 to 8 9 to 10 > 10


Equifax Trans Union


Improved attribute levelingRevolving bankcard

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

0 1 to 2 3 to 4 5 to 6 7 to 8 9 to 10 > 10


Equifax Trans Union

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

0 1 to 2 3 to 4 5 to 6 7 to 8 9 to 10 > 10


Equifax Trans Union

Dual-bureau attribute leveling improved


Delinquency filter leveling Months since derogatory

Equifax (Rate=9) PHR1 = 5 (PHR1 Date 05/2010)Date Reported 04/2011 PHR2 = 5 (PHR2 Date 04/2010) Date of Last Activity 09/2009 PHR3 = 4 (PHR3 Date 03/2010)

Status Date Payment History or PHR

Trans Union (CMOP=09)Date Verified 04/2011 99999999999554321X11Date of Last Activity 12/2009 Date of Payment History: 04/2011

Months Since Derogatory: Report Date 05/2011

Equifax : 06/2010Months Since PHR1 -1: 11

Trans Union : 06/2010Months (pmt history): 11

Months Since Reported: 1 Months Since Reported: 1 STAGG

Premier


Agenda

Attribute overview


Custom models

Improved decisions

Summary

Q & A


Most complex

Most summarizedScores

Aggregate into attributes

Summarize and categorize raw TIPs

Raw trade, inquiry and public record Information

Make data actionable

The process of distilling credit data down to useful and actionable information


Building dual-bureau models

Different approaches for building dual bureau models

► A single model built on combined dual bureau data

► A model tailored to each bureau

► A single model built on a single bureau’s data


Building dual-bureau models

Approach Pros ConsA single model built on combined dual bureau data

� Data from each of the bureaus is represented in the model build

� Variable selection process will help insure that variables which are discriminating across all bureau enter model

� Requires sufficient data from both bureaus

� Must be cautious against bureau bias (any policy rules govern bureau selection?)

A model tailored to each bureau

� Data from each bureau is represented� More robust against unbalanced dual

bureau attributes

� Requires sufficient data from both bureaus

� Alignment of distributions does not insure alignment of individual scores

� More time consuming (two models!)

A single model built on a single bureau’s data

� Requires data from only a single bureau� If attributes are well-aligned, scores

should be reasonably well-aligned� Scores across bureaus will also

correlate well!

� Well-balanced dual bureau attributes are critical to a successful implementation

� Only one bureau’s data is represented in model development


AttributesThe challenge

Introduction of new attributes into application system tends to be a constraint for financial institutions

Existing attributes become embedded in multiple systems and processes

Change becomes difficult and costly

► Impact to existing models must be quantified and measured

► Building new models is time consuming and costly

Leveraging automated analytical processes become a necessity

► Facilitates comparison of multiple attribute sets

► Generates the statistical measurements to measure differences

► Produces forecast charts to quantify savings


Agenda

Attribute overview


Custom models

Improved decisions

Summary

Q & A


Case StudyGeneric Risk Model

Performance flag: Good/Bad flag: 1-30 Good, 90+ Bad

Performance window: 24 months, June 2006 to June 2008

Generic Attributes

Premier Attributes% Premier

LiftStatistics KS KS

Overall Risk-based Segmentation Model

61.97 62.87 + 1.45%

Highest Risk Segment 23.28 24.59 + 5.63%High Risk Segment 18.83 23.00 + 22.15%Low Risk Segment 21.25 25.86 + 21.69%

Lowest Risk Segment 48.00 50.45 + 5.10%

Developed a generic risk model using the new Premier Attributes to highlight improvement over generic credit attributes


Premier Attributes compared to other attributes in the market

In an independent study, a lender experienced:

► A 16% lift using Premier Attributes vs. other credit bureaus attributes

► A 31% lift over using Premier Attributes vs. a traditional modeling shop

Benchmark models were developed with Premier Attributes and a competitor’s attributes

► Overall the benchmark models show Premier Attributes provide consistent lift over the other models using competitor’s attributes

► KS: Premier Attributes scores provided 2% to 4% lift in KS performance

► Worst scoring: Premier Attributes scores provided 2% to 5% lift in bads captured in worst scoring 10% to 20% population


Agenda

Attribute overview

Tri-bureau leveling process

Custom models

Improved decisions

Summary

Q & A


Concluding thoughts

Attributes are commonly overlooked and oversimplified; many analysts know how to build models using attributes, however very few analysts have expertise in understanding the underlying attribute elements

Attributes are the underlying criteria used in almost all aspects of decisioning; so creating attributes is as important as creating your models, decision trees, criteria, etc.

Multiple approaches to building dual bureau solutions – each has benefits and shortcomings

For “single bureau” approach, balanced attributes and equivalent data across bureaus yields similar scoresValidate scorecard performance regularly (at least annually)Establish and regularly review / refine performance targetsRespond appropriately to change


Questions

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