The material in this presentation is the property of Fair Isaac Corporation. This material has been...

The material in this presentation is the property of Fair Isaac Corporation. This material has been provided for the recipient only, and shall not be used, reproduced, copied, disclosed, transmitted, in whole or in part, without the express consent of Fair Isaac Corporation. © 2007 Fair Isaac Corporation. Confidential.

Payment Cards 2007Credit Bureau Scores vs. Internal Score Solutions

in emerging marketsFIC’s View

Esteban Sossa Credit Bureau Solutions, Senior

Consultant Fair Isaac

June 7th 2007

2© 2007 Fair Isaac Corporation. Confidential. 2Copyright © 2003 Fair Isaac Corporation. All rights reserved.

Agenda

Fair Isaac

Definitions

Credit Scoring

CB Scores

Application and use

FIC’s approach

Challenges during Implementation

Q&A

3© 2007 Fair Isaac Corporation. Confidential.

You are likely to touch Fair Isaac technology when you…

Buy or refinance a home Make online purchases Take out auto insurance

Buy or use a cell phone Submit a medical claimUse a credit/debit cardUse an ATM

File for workers’ comp


We are the proven standard in analytics and decision management

65% world’s credit cards managed by Fair Isaac technology

65% world’s credit cards protected by our fraud solutions

75% U.S. mortgages scored by Fair Isaac

70% of U.S. post charge-off processing performed using Fair Isaac collections system

9 of the top 10 Fortune 500 companies

51 of the top 100 banks in world

9 of the top 10 UK banks

99 of the top 100 U.S. banks

49 of the top 50 U.S. card issuers

22 of 25 top U.S. small business lenders

80% of top U.S. personal lines insurers

8 of top 10 U.S. wireline providers

Top 10 U.S. wireless providers0 50 100PERCENTAGE

AMONG OUR CLIENTS


EDM is about improving decisions in an integrated way across the enterprise.

Operational Systems and

Channels

CRMCRM

SCMSCM

ERPERP

Call CenterCall Center

WebsiteWebsite

EmailEmail

POSPOS

Etc.Etc.

Design:EDM Technology

Deployment:EDM System

Rules / StrategiesPredictive Analytics

Data Access Predictive Analytics

Rules Management

Enterprise Data External Data

Results

Decision

Request for Decision

Analyst Tools

Business User Tools


AgendaFair Isaac

Definitions

Credit Scoring

CB Scores

Application and use

FIC’s approach


Q&A


What is Credit Scoring?

“Credit scoring predicts the statistical probability that an account will fall into arrears”

ScoreScore

Bad Rate

HighHighLowLow


The Data-driven strategy roadmap:

Benefit • Brings all predictive analytics into a single decision framework

• Assigns the optimal action for each prospect/account given specific business constraints

• Creates micro segments by matrixing 2 or 3 predictive models

• Rank orders prospects on a single dimension

• Establishes broad segments based on customer profile data

HIGH

Multi-ScoreTrade-OffAssessment

Multi-ScoreTrade-OffAssessment

PredictiveModelsor “Scores”

PredictiveModelsor “Scores”

Profiling &SegmentationProfiling &Segmentation

XXXX

X

X

X X

XXX

X

X

XXX X

X

X

XXXXX X

X

X X

X

XX

X

XX

X

X

X

XX

XXXXX X X

X X

X

X X

X

XX

XX X

XX

X

XX X

X

X

X

X

XX

Incremental ProfitImpactover previous

5-20%5-15%0-15%0-10%

DecisionOptimizationDecisionOptimization


The value of Fair Isaac’s Analytic methodologies

Every year, Fair Isaac has helped banks realize an average annual increase of $2.80 profit per individual account.

Fair Isaac have created methodologies that have helped banks increase profits and control risks across key decision areas.

High

Policies

Score-based Strategies

Adaptive Control

Data DrivenDesign

NPV – BasedEvaluation

Optimisation

Rules

Expert Scores

Pooled MF Scores

Custom MF Scores

Integrated MF + CB Scores

TransactionData Scores

Incremental lift and sustainable competitive advantage

Low

Multiple Outcomes

High

Low

Pro

fita

bil

ity

Fair Isaac spent over $80m on Research & Development last year


Credit Bureau Scores

What is it?


What is in a “Typical”Credit Bureau File?

Retail revolving30 day updateRetail revolving30 day update

Enquiryonline update

Enquiryonline update

Court record datadaily/weekly updateCourt record data

daily/weekly update

Bank instalment/revolving30 day updateBank instalment/revolving30 day update

Sales/personal finance30 day updateSales/personal finance30 day update

Cellular30 day update

Cellular30 day update

Utility30 day update

Utility30 day update

Mail order30 day update

Mail order30 day update


HEADER RECORD (IDENTIFYING INFORMATION)I. Wishfor Credit 12 Lost Lane Sam’s Petrol & Oil

805 Main St. Somewhere, 6666 Attendant

Anytown, 9999 Date of Birth 1965-05-26

PUBLIC RECORDS (LEGAL ITEMS)1999-02-14 Judgment 1000

ENQUIRIES (SEARCHES)Date Subscriber Type2001-04-21 GoodBuy EQ2002-05-10 RocketFin EQ

ACCOUNT INFORMATION (PAYMENT PROFILE)

Supplier Date Date Opening Current Account Payment Reported Opened Balance Balance Type Profile

NiceWear 200206 19970110 700 54 I 1 1 2 2 1 0 0 0 0 0 1 0 0 0 0 0 0 2 2 2 1 1 0 0

SuperCall 200207 19990312 223 120 R P 2 1 0 0 0 0 0 0 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0

Best Bank 200207 19930201 7,500 3,520 R 0 0 0 0 0 0 3 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

Village Bank 200207 20000214 12,000 7,358 I 0 0 0 0 0 0 1 0 2 1 0 0

1

2

3

4

The Value of CB Scores:Distilling the Predictive Power

680


Multiple Model Approach

Negative information

Default information

Inquiry information

Trade line information

Balance information # of accts with

balance > 0

Negative information

Default information

Inquiry information

Trade line information # of trade lines Payment status

Negative information Months since last

delinquency

Default information # of public records

Inquiry information # of inquiries

What Bureau Data is Available?

Multiple Model Technology

Full Shared Data Partial Positive Data Negative Data Only


Categories of Information

Aspects of the Credit Background Measured

Contribution toPredictive Power

Previous credit performance 35%

Current level of indebtedness 30%

Amount of time credit hasbeen in use

15%

Pursuit of new credit 10%

Types of credit available 10%


Example Scorecard

Characteristics Attributes Points

Number of payment profiles 0 15

1 22

2 30

3 40

4+ 30

Number of enquiries 0 75

1 55

2+ 40

Number of months in file below 12 12

12 to 23 35

24 to 47 60

48+ 75

Number of months since No judgement 75

0 to 5 10

6 to 11 15

12 to 23 25

24+ 49

most recent judgment


Credit Scoring-Versions & Applications:

Prospecting (Marketing)

Application (Risk)

Customer Management (Risk/MK)

Collections

Fraud


CB Scores vs. Internal Solutions

CB scores offer a 360 ° view of the market for the customer.

CB scores avoid distortions by self-segmentation.

Internal solutions represent better the strategy of the organisation.

I. solutions can be a strategic advantage when own database is stronger than the rest of the market.

Fair Isaac view is that they are complementary and both are needed.


Credit Bureau Scores Complement Custom Scores

Application or Behaviour ScoreB

ure

au

S

co

reLow

High

High

Strengthen your decisionChange your

decision

Change yourdecision


AgendaFair Isaac

Definitions

Credit Scoring

CB Scores

Application and use

FIC’s approach


Q&A


The use of Scoring (Strategic view) risk/return profile of retail credit portfolios

Risk Based Pricing Up or Cross-Selling Campaign Limit Management

portfolio auditing Business issues: Profitability and room for improvement Increase acceptance rates Consider potential new types information to collect Consider alternative score development and management methods Basel II certification process


600

620

640

680

Credit Bureau Scores Rank Consumer Risk


How Does the Solution Work?Summary of Models and Software

Scoring Models Combines worldwide bureau data

experience Tested against multiple datasets

Implementation Software Receives and processes input data Generates characteristics Applies the right model Generates scores Returns scores and reason codes

Documentation Installation instructions Interface specification Global FICO® scores user’s guide

Support Initial training and consultancy Helpdesk Fair Isaac analytics consultancy -

validations Systems integration consultancy


Ongoing Development Path

Global FICO® Score

NowNow

Refine alignment

capabilitiesacross

countries

Demonstrateperformance

in even more

countries

Implement in multiplecountries

Develop,validate

and refinemodels


600

620

640

680

Credit Bureau Scores Rank Consumer Risk


Use Through the Credit Lifecycle

ACCOUNTACQUISITION

ACCOUNTMANAGEMENT

APPLICANTSCREENING

Approve/decline

Setting initial credit limits

Tiered pricing of loans

Solicitation

Cross-sell

Portfolio acquisition

Limit increase/decrease

Authorisations

Collections

Reissue


Aligned across enterprise on: Target customer segments Size of opportunity Appropriate risk Winning sales/service propositions Functional roles

Relevant, personalised experiences: Guide each touchpoint & interaction Excel at ‘moments of truth’ Shape beliefs & attitudes

Targeting right customers: Messages, channels & timing Sales & marketing integration Closed-loop learning

Customer insight: Value Desired experience Propensities/responsiveness Buying process & behavior

Customer Segmentation & GFS

Enterprise Customer Strategy

Customer Experience

Effectiveness

Sales & Marketing

Effectiveness

Customer Data & Analysis

MarketingAnalytics

Continuous Learning &

Improvement


State of the Art Segmentation

ClusterBots

Cluster Analysis

DecisionTree A

B

C

D

E

F

G

Association Rules

[Age] [Beer]

[Shopping Frequency]

Under 30

Normal HighLow

A

BAbove 30

Multi-dimensional unsupervised approach that reveals natural groupings of customers within a collection of data

Supervised tree approach where the end-points of which represent segments or sub-segments

Basic multi-dimensional analysis approach to group logically or naturally related items together and describe common properties

Multi-dimensional supervised approach that groups individuals into clusters based on their similarities, while maximising segment differences


Simplified decision model for contractual repricing optimization

ProfitProfitPricingChange

$ Loss$ Loss

$ Rev$ Rev

Current Balance

BehaviorScore

Current Pricing

Utilization

BureauInfo

AttritionAttrition

BalancesBalances

Good / BadGood / Bad

Can be expanded to include credit line and fee changes


Example Data AttributesCredit Card Portfolio Segmentation Model

Reward programmeCurrent balance, avg

balance, reward preferences, last

redeemed, primary source

DemographicsAge, income, gender, household size, region

BehavioursSeasonality, categories

and bundles, cash withdrawals

Member AttributesOpt-ins, APR, annual fee,

card type, bus vs personal, total limit

ResponsivenessMarketing treatments and

responses

Credit Bureau Score


AgendaFair Isaac

Definitions

Credit Scoring

CB Scores

Application and use

FIC’s approach


Q&A


Modeling Tools

Typical scoring development pains

ProductionSystem

ProductionData

Other DataSources

ModelingDataset

CreateVariables

BuildModel

ValidateModel

ModelSpec.

CodeModel

CodeVariables

TranslateMetadata

TestModel

Production IT

Modeler

DeployModel

OtherModelers

ETL(IT)

Assorted filesand scripts

Software Tools

Copy

4Deployment

Delays

1

DelaysGetting Data

2Limited

Technology

5 ModelValidity

3Inefficient

Process


Scoring Building Blocks

5020-50% 4250-80% 38

35> 80%No Info 40

< 20%

Scorecard Module

Optional Modules

Data Preparation

Source Management

Sorting

Extraction & Load

Merging & Collation

Cleaning & Transformation

Sampling & Holdouts

Data Analysis

Variable Creation

Variable Binning

Statistical Data Analysis

Probability Table Generation

Correlation & Factors

Graphing & Charting

General Models

Variable Selection

Regression Models

Neural Network Models

Principal Components

Blended Models

Custom Models

Model Validation

Performance Analysis

Explanation Tools

Statistical Results Analysis

Performance Charting

Model Comparison & Selection

Unit Test Tools

Model Deployment

Model Instrumentation

Model Validation Reports

Real-Time Profiling

Runtime Alarming

Java Runtime Generation

XML Export

Interpretable Scorecard

Weights Engineering

Interactive Classing

Variable Selection

Reject Inference

Performance Reporting


Training and data quality are vital Standard techniques

Logistic regression Linear regression Neural network And more…

Advanced techniques Advanced scorecard technology. Auto-Variable creation. Automated “Expert” binning & Interactive Binning. Sample Bias Adjustment: Performance Inference Define Special Values and constrain their contribution. And a whole lot more…


Continuously Improve Business Results

• Runtime diagnostics provide insight into model quality and efficacy

• Results point out where to tune model

• Alerts if model decays over time

• Model easily tuned in Model Builder

Score

Check Data

Quality

MonitorDegradatio

n

Data

Model Scoring


Scoring Lifecycle Management

Continuous Improvement

Works with your applications

Minimize Modeling and Deployment Risk

More Scores from less peopleIntegrated environment from development through deployment

Task Automation and Object Reuse

Runtime model diagnostics and alarms

Access to New Techniques / Innovation

Quick to deploy into Decision Engine and Workflow applications

What you need How you get it

No manual hand-off to IT

Test code before going into production

Regulatory compliant scorecards

Customisable reports and documentation both for development and monitoring

Meet Regulatory Requirements


Some conclusions:

Data-driven strategies can produce real value.

True automation is not possible without proper data management.

Well informed managers produce better decisions, and faster.

Learning curve is softer for everybody in the organisation if lessons supported by measurable facts.


DATA

INFORMATION

INTELLIGENCE

STRATEGY

PROFITS !!!

FIC’s view of Analytics


Creating a new Decision Model across the organisation.

Map the decision model

Re-engineer the decision model

Provide measurable & realistic scenarios (consider business constrains)

Select desired scenario (s)

Implement & execute

Learn from results

Do it again from the beginning


Auditing & Improvement Process:

Decision Modeling

Optimizationand Simulation

Accelerated Learning

Interpretation

Establish mathematical relationships between customer treatment options, their reactions and profitabilityTest efficiently to learn

beyond historical operating regions to further increase future performance

Gain insight into key profit drivers and opportunity pockets through diagnostics and final strategy engineering

Identify optimal strategy scenarios subject to your multiple goals and business constraints as well as your forecasts for the future


The Decisioning roadmap to impact ROI

Analytic Applications

World Class Analytic Skills

InnovationPartnership

Project Support

Create better models

Better understand consumer behavior

Improve business planning

Build & retain distinctive teams with strong analytic and business skills

Adopt standard processes and methodology

Manage emerging regulatory and compliance requirements

Improve productivity

Automate processes, decisioning and reporting

Improve speed to market

Provide analytic capacity to meet peak demand

Outsource routine model development

Leverage third-party for more effective QA & Audit


Fair Isaac focus on helping youmake smarter decisions.

Fair Isaac is the leader in decision management powered by advanced analytics

Our solutions unlock value for people, businesses and industries

The material in this presentation is the property of Fair Isaac Corporation. This material has been provided for the recipient only, and shall not be used, reproduced, copied, disclosed, transmitted, in whole or in part, without the express consent of Fair Isaac Corporation. © 2007 Fair Isaac Corporation. Confidential.

THANK YOU

Esteban Sossa

+44 793 919 5111

+48 602 447 [email protected]

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