The Value of Connected Decisions -...

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.© 2009 Fair Isaac Corporation. 1

The Value of Connected Decisions

David Lightfoot Vice PresidentFair Isaac

David ParisAssociate PartnerIBM

© 2009 Fair Isaac Corporation.2

Top 10 Business Drivers, Strategic Responses, and Technology Initiatives in Retail Banking (2009)

Business Drivers1. Current economic

environment2. Regulatory

change and compliance

3. Competitive threats

4. Changing customer preferences

5. Revenue growth

6. Operational efficiency 7. Business growth/ contraction

8. Customer loss/ dissatisfaction

9. Fraud and financial crime

10. Information security

Strategic Responses

Developing short/long- term strategies to mitigate losses

Increased oversight to ensure compliance

Targeted pricing and product initiatives

Focused investments/ disinvestments in delivery channels

Improving customer acquisition, retention, cross-sales capability

Company-wide cost reduction initiatives; rightsourcing

Identifying and exploiting new market opportunities

Capital preservation Increased cooperation with other banks and third parties

Tighter controls and increased audits of data access

Technology Initiatives

Upgrade loan processing- modification/ collections/foreclosure processing

Modify systems; deploy new processes for compliance

Improve analytics and performance management

Support new product and channel initiatives, divestitures

Enable a single customer view; improve customer analytics

Automate/streamline processes; employ software as a service (SaaS); outsource; consolidate systems

Implement core systems renewal, service-oriented architecture (SOA) capabilities

Automate problem tracking and resolution

Support improved fraud detection and risk analysis

Improve data access controls and data tracking; expand use of encryption

Automate/streamline processes; employ software as a service (SaaS); outsource; consolidate systems

Implement core systems renewal, service-oriented architecture (SOA) capabilities

Improve analytics and performance management

Enable a single customer view; improve customer analytics

Support improved fraud detection and risk analysis

Developing short/long- term strategies to mitigate losses

Increased oversight to ensure compliance

Capital preservation

Exhibit #: 57:04R-E2Source: TowerGroup


What is a “Connected Decision”?

» A decision executed within a specific Decision Management application or lifecycle area, made smarter through leveraging insights and capabilities from another application or lifecycle area, such as» Data» Shared decision logic and scores» Advanced analytic capabilities» Integrated and shared workflows


9 Ways Connected Decisions Bring Value

1. Enhanced rule management and scoring in Collections………….. $7M

2. Identify First Party Fraud in originations and early in the lifecycle………………………………………………………….. $6M

3. Easily extend the use of external data in collections………………. $4M

4. Identify First Party Fraud among collection cases…………………. $2.4M

5. Improve account management with integration of transaction based scores……………………………………………... $2.3M

6. Improve collections through assessing the impact of operational negation…………………………………………………… $1.6M

7. Improve agency assignment in collections………………………….. $1.1M

8. Improve early account management and cross-sell………………... $827K

9. Improved on-going strategy management…………………………… Priceless

Value*

*1 Year Benefit based on: client portfolio of 2mm accounts with average balance of $2k and industry standard bad debt and interest income rates.


Decision Management Suite Benefits

Improved results through…

Connected Technology: Drive cost and risk out of technology adoption

Connected Decisions: Making better individual decisions

Connected Strategy Adaptive control and business Management: simulation across lifecycle and portfolios

© 2009 Fair Isaac Corporation.6 © 2008 Fair Isaac Corporation..6

Agenda

» Business Challenges» Value of Connected Technology

» Value of Connected Decisions

» Value of Connected Strategy Management

» Achieving Connected Decisions


» Drive Risk and Cost out of Technology Adoption

Connected Technology


Suite Architecture — High Level

Smarter decisions — across the lifecycle

Marketing Origination CustomerManagement

Collections& Recovery Fraud

Decision Management ArchitectureCommon Components

Decision Management Application Suite

Data Model Model and RulesRepository Case Management ReportingRules Management


Architecture — High LevelCommon Data Sourcing and Use>> Reduced Data Application & Server Investment Plus Operational Time to Access and Integrate



Decision Management Application Suite

Standard Data Content & Dimensions

» Client type» Product type» Org unit» Generic

attributes» Probabilities» Wallet size

» Client type » Product type» Org unit » Capacity to

pay» Loan amount» Risk Rating

» Client type » Product types» Org unit » Product

balances » Specific

attributes

» Client type» Product type» Org unit» Product

balance» Capacity to

pay

» Client type» Product type» Org unit


Suite Architecture — High LevelFunctional Component Re-use>> Minimize Functional Application and Hardware Investment, Plus Simplifies Workflow

Common Architecture Components

Key Business Criteria & Attributes

»Logical & physical

»Common hierarchy, relational and metadata approaches

»Deals with clients, products, organization units and the like

»Applied by client, product, org unit, other attributes

»Relationally- sequenced and routed

»Use-case driven

»Linkage between model used and rules applied

»Common metadata for use consistency

»Rules-based»Control and

routing driven by attribute and relational elements

»Dimensions by client, product, org unit, other attributes

Data Model Model and RulesRepository

Case Management ReportingRules

Management




Increase IT Efficiency

» Growth — organic and manage acquisitions» Common functional architecture> easier

to identify ‘gaps’ for ‘plug and play’ capacity remediation/increase

» Efficiencies — agility, speed, and cost management IT strategies» Common hardware and middleware>

greater load balancing flexibility and server capacity optimization

» Speed — faster to market — smaller, more manageable projects» Add business application to common

hardware & middleware platform> simpler implementation and faster ‘into production’ cycle


Reduce System Ownership Costs

BeforeClient ITCosts

InstallationFees

LicenseFees

Client ITCosts

InstallationFees

LicenseFees

“Internal Costs”Costs

Application Investment

AfterBefore


Reduce System Ownership Costs and Time to Deployment

Two » Application servers » Application server

software copies» Server administration

costs » Server ‘footprint’ costs

» Data center space» Energy costs

Before ‘Connected Architecture’

Recent Sample Installation of Debt Manager & TRIAD in UKAfter ‘Connected Architecture’One » Application server* » Application server

software copy» Server administrator » Server ‘footprint’

» Data center space» Energy costs

*UNIX accomodating multiple application and operating system loads

Client ITCosts

Client ITCosts

“Internal Costs”

Plus » Reduced ‘one-off’ time to

» Source» Configure» Install

» Uniform architecture >> » Managed

infrastructure ‘hosting’ opportunity

» Managed service ‘hosting’ opportunity

» Reduced data center space & energy use improves ‘green agenda’ compliance


» Making Better Individual Decisions

Value of Connected Decisions


Case Example 1

Certain Client Types Tend to Result in Certain Risk Profiles . . .



Decision Management Application Suite» Comprehensive Data








attributes



pay



Case Example 1

Helping to Determine Average Utilization and Drawdown . . .




»Combining real time with historical information








attributes



pay



Case Example 1

Which Could Exceed or Align With Actual Capacity to Re-Pay . . .




»Combining real time with historical information»Shared decision logic and scores








attributes



pay



Case Example 1

Leading to Possible Fraud to Avoid or Reduce Repayment . . .




»Combining real time with historical information»Shared decision logic and scores»Integrated and shared workflows








attributes



pay



Case Example 1

This Should then be Fed Back to Adjust Marketing by Client Types




»Combining real time with historical information»Shared decision logic and scores»Integrated and shared workflows

»Balancing strategic and operational decisions








attributes



pay



Case Example 2: Attacking First Party Fraud

» Losses compared to Third Party fraud?

» How many collections cases really FP Fraud?

» Performance of Detection Solution?.

» 100 BP vs 10-12

» 50% FPF loss reduction

» 10-20%


First Party Fraud Solution Requirements

» Information across accounts and lifecycle — history and real-time

» Best data, analytics, investigative capabilities — anticipate & combine

» Mitigate risk across the lifecycle — protect relationships and revenue


The Building Blocks of a Better First Party Fraud Solution

Data

DataData Prediction engine

Multiple transaction scores

Multiple transaction scores Adaptive ModelsAdaptive Models Call Transaction Scoring

Engine from Application Call Transaction Scoring Engine from Application

Integrated Account Treatment

Workflows

Integrated Account Treatment

WorkflowsCommon

data model Common

data modelShared

Policy Logic Shared

Policy Logic

StatusActions

ActionsActions

workflow

Data capture opportunities

Investigation workflows


Cutting First-Party Fraud at the Source

YES

NO

Is Application Approved and

Accepted by customer?

Book Loan to Client Host System and Assign Account #

Send application data (with application ID & acct #) and Risk

Scores

Is profile initiated?

Initiate and update profile with data and score

Update profile with data and score

Does score indicate high probability of

suspect fraud?

Do rule conditions of transaction &

other data fulfill case creation logic?

Dispositions and Actions stored for

future risk segmentation and

fraud modeling and employee

tracking

First-Party Fraud Identify Data

collected for file update

Case Generates

Fraud Analyst Investigates for first-party

fraud

Analyst dispositions

case and takes

appropriate action

Originations Process

New account’s monetary & non-monetary transactions

scored leveraging application data/score

Originations

Fraud

Client Host System Provides Acct #s to

Application IDs

First-Party Fraud Identify Data is updated into Originations Fraud Check lookup tables

YES

YES

YES

NO

Send application data (with application ID & acct #) and Risk

Scores


Initiate and update profile with data and score

Update profile with data and score

Fraud

YES

Dispositions and Actions stored for

future risk segmentation and

fraud modeling and employee

tracking

First-Party Fraud Identify Data

collected for file update

Originations Process

First-Party FraudIdentify Data is updatedinto Originations FraudCheck lookup tables

First-Party FraudIdentify Data is updatedinto Originations FraudCheck lookup tables

Does score indicate high probability of

suspect fraud?

Do rule conditions of transaction &

other data fulfill case creation logic?

Case Generates

Fraud Analyst Investigates for first-party

fraud

Analyst dispositions

case and takes

appropriate action

New account’s monetary & non-monetary transactions

scored leveraging application data/score

YES

YES


Identifying Fraud Among Collection Cases

Fraud


Has contact been made via phone?

Is account First

Payment Default (FPD)?

# Call Attempts >X

Has Mail been Returned?

Confirmed Fraud?

Queue Collection

Case for Call

Route Collection

Case to Skip Trace

Confirmed first-party fraud abuse. Move to

Collections Fraud Process

Confirmed Non Fraud. Continue Skip Trace

Suspect Fraud — Continue Skip Trace

Initiate and update profile with data and

score

Update profile with data and

score

Case Generates

Dispositions and Actions stored for future risk segmentation

and fraud modeling

Collections rule (based on score or

hotlist) fires. Generates case.

Fraud score or other rule generates case.

Collections data supplements investigation.

Model scores high based on collections score and/or data. Generates case.

Store data into

database

Confirmed first-party fraud

abuse. End Case.

Confirmed Non Fraud. End Case

Suspect Fraud —

Case still open

Fraud Analyst Investigates and

takes appropriate

actions/disposition

Collections and Recovery

Updating Collections with Fraud

Analysis Results

YESNO

YES

NO

YES

NO

YES

Fraud


# Call Attempts >X

Has Mail been Returned?

Initiate and update profile with data and

score

Update profile with data and

score

YES

NO

YES

Collections rule (based on score or

hotlist) fires. Generates case.

Fraud score or other rule generates case.

Collections data supplements investigation.

Model scores high based on collections score and/or data. Generates case.

Store data into

database

Confirmed Fraud?

Route Collection

Case to Skip Trace

Confirmed first-party fraud abuse. Move to

Collections Fraud Process

Confirmed Non Fraud. Continue Skip Trace

Suspect Fraud —Continue Skip Trace

Case Generates

Dispositions and Actions stored for future risk segmentation

and fraud modeling


abuse. End Case.


Suspect Fraud —

Case still open


abuse. End Case.


Suspect Fraud —

Case still open

Fraud Analyst Investigates and

takes appropriate

actions/disposition

Updating Collections with Fraud

Analysis Results


Quantifying Value of FPF Solution

Value Estimated Benefits *

Reduced First Party Fraud Losses by 20% in Originations and Early Detection through integration of Originations and Transaction Fraud solution

$6 million

Reduced First Party Fraud Losses by an incremental 10% through integration of Collections and Transaction Fraud solution

$2.4 million

First Year Total Savings from both $8.4 million

*Based on: client portfolio of 2mm accounts with average balance of $2k and industry standard bad debt and interest income rates


Estimated Client Value of Other Connected Decisions

Connected Decisions Expected Year 1 Benefit*

Improve early account management and cross-sell originations through cross-leveraging data and scores in originations and customer management

$827k

Improve customer management decisions with use of a transaction score. $2.3mm

Improve collections through assessing impact of operational negation. $1.6mm

Enhanced rule management and scoring in collections $7.1mm

Improved agency assignment $1.1mm

Easily extend the use of external data in collections $4.1mm

*Based on: client portfolio of 2mm accounts with average balance of $2k and industry standard bad debt and interest income rates


» Adaptive Control and Business Simulation Expanded Across Lifecycle and Portfolios

Value of Connected Strategy Management


Enabling Connected Strategy Management

» Ability to:» Universally manage adaptive control» infer and hypothesize root cause - generate new approaches» Isolate cause and effect across lifecycle» Evaluate future business results» Simulate future results through stress testing


DM SUITE SOURCE 0 - 3 MOB 4 - 6 MOB 7 - 12 MOB 13 - 24 MOB 25+ MOBValue Value Value Value Value

(#) Mailed Capstone % Response Capstone

% Approval Capstone % Net Response Capstone

Avg. FICO (NR) Capstone

Avg. U/W Score (NR) Capstone

(#) Accounts TRIAD

Avg. Balance TRIAD

Avg. Line TRIAD

% FPD TRIAD

(#) Entry-Rate TRIAD (2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

($) C/O (ann) TRIAD

(#) C/O (ann) TRIAD

(#) Accounts TRIAD Total Balance TRIAD

Total Credit Line TRIAD Avg. Balance TRIAD

Avg. Line TRIAD Avg. FICO TRIAD

(%) Inactive TRIAD Auth. Approval TRIAD

(#) CLI TRIAD (#) CLD TRIAD

(#) Attrition TRIAD Bankruptcy TRIAD

(#) Entry-Rate TRIAD (2+) DQ (#) TRIAD (2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD ($) C/O (ann) TRIAD

(#) RR 1 - 2 DM / TRIAD (#) RR 2 - 3 DM / TRIAD (#) RR 3 - 4 DM / TRIAD (#) RR 4 -5 DM / TRIAD ($) RR 1 - 2 DM / TRIAD ($) RR 2 - 3 DM / TRIAD ($) RR 3 - 4 DM / TRIAD ($) RR 4 -5 DM / TRIAD

Avg, DQ Balance DM / TRIAD Avg. Payment DM / TRIAD

Flow-Through Rate Calculated

($) Recovered DM / TRIAD Recovery Rate DM / TRIAD

($) Chargeoff Falcon Sales TRIAD

Recovery Falcon Net Fraud Loss Rate Falcon False-Positive Rate Falcon

Finance Charge Yield Calculated Non-Interest Income Calculated

Loss Rate Calculated Risk-Adjusted Yield Calculated

RECOVERY METRICS

FRAUD METRICS

PROFITABILITY METRICS

UNDERWRITING METRICS

ORIGINATIONS METRICS

CUSTOMER MGT. METRICS

COLLECTIONS METRICS

Holistic Strategy Evaluation

Avg. FICO (NR) Capstone Avg. U/W Score (NR) Capstone

(#) Accounts TRIAD

Avg. Balance TRIAD

Avg. Line TRIAD

% FPD TRIAD

(#) Entry-Rate TRIAD

(2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

($) C/O (ann) TRIAD

(#) C/O (ann) TRIAD



Total Credit Line TRIAD

Avg. Balance TRIAD

Avg. Line TRIAD

Avg. FICO TRIAD

(%) Inactive TRIAD

Auth. Approval TRIAD

(#) CLI TRIAD

(#) CLD TRIAD

(#) Attrition TRIAD

Bankruptcy TRIAD


(2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD

($) C/O (ann) TRIAD



Recovery Falcon

Net Fraud Loss Rate Falcon

False-Positive Rate Falcon

FRAUD METRICS


Strategy Evaluation Example: Impact of Transaction Fraud Strategy on Sales and Attrition

D M SUITE SOURCE 0 - 3 M OB 4 - 6 M OB 7 - 12 M OB 13 - 24 M OB 25+ M OBValue Value Value Value Value



Avg. Balance TRIAD

Avg. Line TRIAD

Avg. FICO TRIAD

(%) Inact ive TRIAD

Auth. Ap proval TRIAD

(# ) CLI TRIAD

(# ) CLD TRIAD

(# ) At trit ion TRIAD

Bankrup tcy TRIAD


(2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD

($) C/O (ann) TRIAD

(# ) RR 1 - 2 DM / TRIAD

(# ) RR 2 - 3 DM / TRIAD

(# ) RR 3 - 4 DM / TRIAD

(# ) RR 4 -5 DM / TRIAD

($ ) RR 1 - 2 DM / TRIAD

($ ) RR 2 - 3 DM / TRIAD

($ ) RR 3 - 4 DM / TRIAD

($ ) RR 4 -5 DM / TRIAD

Avg, DQ Balance DM / TRIAD

Avg. Paym ent DM / TRIAD


($) Recovered DM / TRIAD

Recovery Rate DM / TRIAD

($ ) Chargeof f Falcon Sales TRIAD

Recovery Falcon


False-Posit ive Rate Falcon

CUSTOM ER M GT. M ETRICS

COLLECTIONS M ETRICS

RECOVERY M ETRICS

FRAUD M ETRICS

# ATTRITION TRIAD -10%

$ CHARGE OFF FALCON +0.01%SALES TRIAD +15%

FALSE-POSITIVE RATE FALCON - 0.05%


The Suite Difference

Before

» Fraud strategies usually not tested in champion/challenger mode

» If done, tests were not set up to isolate the effect of Fraud strategy on non-Fraud metrics from the effect of customer management strategies

After

» Champion/ challenger strategies indicate impact of fraud strategies on sales and attrition

» Fraud strategies chosen based on overall business impact, not just improving fraud detection


Ability to Project Future Business Results: Impact of Marketing and Originations Strategies on Collections


(#) Mailed Capstone % Response Capstone % Approval Capstone

% Net Response Capstone


(#) Accounts TRIAD

Avg. Balance TRIAD

Avg. Line TRIAD

% FPD TRIAD


(2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

($) C/O (ann) TRIAD

(#) C/O (ann) TRIAD



Avg. Balance TRIAD

Avg. Line TRIAD

Avg. FICO TRIAD

(%) Inactive TRIAD


(#) CLI TRIAD

(#) CLD TRIAD

(#) Attrition TRIAD

Bankruptcy TRIAD


(2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD

($) C/O (ann) TRIAD




# MAILED CAPSTONE +5 %% RESPONSE CAPSTONE +5%% APPROVAL CAPSTONE +2%

AVG FICO (NR) CAPSTONE -3ptsAVG U/W SCORE (NR) CAPSTONE -5pts


Ability to Project Future Business Results: Impact of Marketing and Originations Strategies on CollectionsDM SUITE SOURCE 0 - 3 MOB 4 - 6 MOB 7 - 12 MOB 13 - 24 MOB 25+ MOB

Value Value Value Value Value




(#) Accounts TRIAD Avg. Balance TRIAD

Avg. Line TRIAD % FPD TRIAD


($) C/O (ann) TRIAD (#) C/O (ann) TRIAD









(#) RR 1 - 2 DM / TRIAD

(#) RR 2 - 3 DM / TRIAD

(#) RR 3 - 4 DM / TRIAD

(#) RR 4 -5 DM / TRIAD

($) RR 1 - 2 DM / TRIAD

($) RR 2 - 3 DM / TRIAD

($) RR 3 - 4 DM / TRIAD

($) RR 4 -5 DM / TRIAD


Avg. Payment DM / TRIAD










COLLECTIONS METRICS

RECOVERY METRICS

FRAUD METRICS



















(#) RR 1 - 2 DM / TRIAD

(#) RR 2 - 3 DM / TRIAD

(#) RR 3 - 4 DM / TRIAD


($) RR 1 - 2 DM / TRIAD

($) RR 2 - 3 DM / TRIAD

($) RR 3 - 4 DM / TRIAD













COLLECTIONS METRICS

RECOVERY METRICS

FRAUD METRICS













Avg. Line TRIAD

Avg. FICO TRIAD


(#) CLI TRIAD

(#) CLD TRIAD



(2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD

($) C/O (ann) TRIAD

(#) RR 1 - 2 DM / TRIAD

(#) RR 2 - 3 DM / TRIAD

(#) RR 3 - 4 DM / TRIAD


($) RR 1 - 2 DM / TRIAD

($) RR 2 - 3 DM / TRIAD

($) RR 3 - 4 DM / TRIAD













COLLECTIONS METRICS

RECOVERY METRICS

FRAUD METRICS




(#) Mailed Capstone

% Response Capstone

% Approval Capstone% Net Response Capstone


(#) Accounts TRIAD

Avg. Balance TRIAD

Avg. Line TRIAD

% FPD TRIAD


(2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

($) C/O (ann) TRIAD

(#) C/O (ann) TRIAD



Avg. Balance TRIAD

Avg. Line TRIAD

Avg. FICO TRIAD

(%) Inactive TRIAD


(#) CLI TRIAD

(#) CLD TRIAD

(#) Attrition TRIAD

Bankruptcy TRIAD


(2+) DQ (#) TRIAD

(2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD

($) C/O (ann) TRIAD

(#) RR 1 - 2 DM / TRIAD

(#) RR 2 - 3 DM / TRIAD

(#) RR 3 - 4 DM / TRIAD


($) RR 1 - 2 DM / TRIAD

($) RR 2 - 3 DM / TRIAD

($) RR 3 - 4 DM / TRIAD








Recovery Falcon


False-Positive Rate Falcon


Loss Rate Calculated

Risk-Adjusted Yield Calculated



COLLECTIONS METRICS

RECOVERY METRICS

FRAUD METRICS



» Historical

» + 6 Months

» + 1 Year

» + 2 Years

(#) RR 1 - 2 DM / TRIAD(#) RR 2 - 3 DM / TRIAD(#) RR 3 - 4 DM / TRIAD(#) RR 4 -5 DM / TRIAD($) RR 1 - 2 DM / TRIAD($) RR 2 - 3 DM / TRIAD($) RR 3 - 4 DM / TRIAD($) RR 4 -5 DM / TRIAD

Avg, DQ Balance DM / TRIADAvg. Payment DM / TRIAD




Before

» Champion/ challenger strategies evaluations compared metrics within lifecycle only

» Evaluations take into account historical results only

After

» Champion/ challenger reports indicate impact that changes in one part of lifecycle have on rest

» Future projections provide earlier assessment of potential impact

» Strategies with undesired impacts can be rejected or improved earlier


One Step Further: Integrated Portfolio Stress Testing

» + 6 Months

» + 1 Year

» + 2 Years

















(#) RR 1 - 2 DM / TRIAD

(#) RR 2 - 3 DM / TRIAD

(#) RR 3 - 4 DM / TRIAD


($) RR 1 - 2 DM / TRIAD

($) RR 2 - 3 DM / TRIAD

($) RR 3 - 4 DM / TRIAD












COLLECTIONS METRICS

RECOVERY METRICS

FRAUD METRICS













Avg. Line TRIAD

Avg. FICO TRIAD


(#) CLI TRIAD

(#) CLD TRIAD



(2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD

($) C/O (ann) TRIAD

(#) RR 1 - 2 DM / TRIAD

(#) RR 2 - 3 DM / TRIAD

(#) RR 3 - 4 DM / TRIAD


($) RR 1 - 2 DM / TRIAD

($) RR 2 - 3 DM / TRIAD

($) RR 3 - 4 DM / TRIAD












COLLECTIONS METRICS

RECOVERY METRICS

FRAUD METRICS













Avg. Line TRIAD

Avg. FICO TRIAD


(#) CLI TRIAD

(#) CLD TRIAD



(2+) DQ ($) TRIAD

(#) C/O (ann) TRIAD

($) C/O (ann) TRIAD

(#) RR 1 - 2 DM / TRIAD

(#) RR 2 - 3 DM / TRIAD

(#) RR 3 - 4 DM / TRIAD


($) RR 1 - 2 DM / TRIAD

($) RR 2 - 3 DM / TRIAD

($) RR 3 - 4 DM / TRIAD












COLLECTIONS METRICS

RECOVERY METRICS

FRAUD METRICS



Finance Charge Yield CalculatedNon-Interest Income Calculated

Loss Rate CalculatedRisk-Adjusted Yield Calculated

Impact of Unemployment + 5%

Finance Charge Yield CalculatedNon-Interest Income Calculated

Loss Rate CalculatedRisk-Adjusted Yield Calculated

Impact of GDP - 10%



Before

» Champion/ challenger strategies evaluations compared metrics within lifecycle only

» Evaluations take into account historical results only

» Evaluations assume similar exogenous conditions

After

» Future projections provide earlier assessment of potential impact

» Stress testing enables comparison of strategies under varying economic conditions

» Stress testing becomes standard practice embedded within your decision management infrastructure

» Strategies with undesired impacts can be improved or rejected earlier

» Worst case scenarios can be avoided or mitigated


Agenda

» Business Challenges» Value of Connected Technology

» Value of Connected Decisions

» Value of Connected Strategy Management

» Achieving Connected Decisions


Forward-Looking Statements

Product roadmaps and similar marketing materials should be considered forward looking and

subject to future change at Fair Isaac’s discretion.

Future functionality, features or enhancements as shown are Fair Isaac’s current projections of the product direction,

but are not specific commitments or obligations.


Advanced Analytic Capabilities to Leverage TODAY

» Adaptive analytics

» Decision modeling

» Economic modeling / Portfolio stress testing


Application Releases

2008 2009 2010

FICO Debt Manager™ 7

FICO TRIAD™ 8.4

FICO Falcon™ Fraud Manager 6 – Scoring Server pre-release

FICO Falcon™ Fraud Manager 6

PlacementsPlus 5.6

FICO Debt Manager™ 8

FICO Originations Manager

FICO TRIAD™ Profit Manager 9

»Enhanced rule management & scoring in Collections

»Connect loss mitigation to customer retention

»Spot misaligned decisions across decision points

»Adaptive Analytics

»Reduce first party Fraud Losses through connections to collections

»Improve customer management decisions with transaction scores

»Improve Agency assignment

»Identify fraud among collections cases

»Cut first party fraud at the source

»Collect more while retaining more customers

»Easily extend the use of external data in Collections


Conclusion

Connected Technology: Drive cost and risk out of technology adoptionConnected Decisions: Making better individual decisions

» Comprehensive data» Shared decision logic and scores» Advanced analytic capabilities» Integrated and shared workflows

Connected Strategy Management:

Adaptive control and business simulation across lifecycle and portfolios» Universal management of adaptive

control strategies» Ability to infer and hypothesize root cause» Ability to isolate cause and effect» Ability to evaluate future business

impact of strategies» Ability to simulate future results through

stress testing

Improved results through…


Learn More at InterACT

Related Sessions» Optimizing Decisions in Turbulent Times» Take Control of Portfolio Performance through Stress Testing» The Economics of Collections and Recovery» A Practical Approach to Enterprise Fraud Management» Analytic Innovations in Fraud Detection» When Customers Attack: First Party Fraud» Connected Customer Management with Collections» Does “Test and Learn” Determine a Nation’s Credit Health?

Products in Expo» Debt Manager 7.0, Falcon 6.0

Experts at InterACT» Brad Jolson, Jose Tagunicar, Doug Clare, David Lightfoot, David Paris IBM

Discussion Online» decisions.fairisaac.com/interact


FICO can help you make every decision count»Control risk and fraud»Grow profitably»Empower your customers

www.fico.com

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.© 2009 Fair Isaac Corporation. 44

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