FICO® Application Fraud Solution

© 2015 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.© 2015 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.

FICO® Application Fraud Manager

Cindy WhiteSenior Director Product [email protected]

www.fico.com/enterprisefraud

http://www.fico.com/enterprisefraud

© 2015 Fair Isaac Corporation. Confidential. 2

Fraud and Financial Crime Trends

535 million consumers acrossthe globe will make a purchase

via mobile this year.1

Account Takeover now attributes for

40% of eCommerce Fraud.2

The FBI pursuing Russian hacker who

amassed a trove of 1.2 billion stolen online

credentials, plus payment card data and Social Security numbers

Every two seconds,there is a new identity

fraud victim in the U.S.3

1Goldman Sachs2Forrester Research: eCommerce Fraud Management Solutions 2014 3 2015 Identity Fraud Study by Javelin Strategy & Research


The Impact of Fraud Goes Beyond Fraud Loss and Operational Costs

Source: ISMB Faces of Fraud Survey, 2013 and 2014, CEB analysis

Customers Are Less LoyalAfter Fraud Incident

0.3%1.3%

2.1%2.3%2.4%

2.8%2.9%3.0%3.0%

3.6%3.6%

4.1%4.2%

5.9%6.1%6.2%Pharmaceutical

FinancialHealthcare

ServicesTechnology

CommunicationsIndustrial

TransportationConsumerHospitality

EnergyEducationResearch

MediaRetailPublic

Abnormal Churn Rates Due to Fraud, by IndustryPercent of Abnormal Customer Churn, 2014

Source: Ponemon Institute

Banks Lack Confidence in Preventionof Account Takeover Fraud

36%

50%

26%

40%

Confidence Versus Frequency of Account TakeoverPercentage of Respondents, 2013–2014

ReportedFrequency

Confidence inAbility to Prevent

20132014


Risk Officers Take on Diverse Challenges

Reduce customer

impact and include

consumers as an extra layer of

defense

CustomerExperience

Help me to be more compliant

and reduce brand risk

Compliance and Reputational

RiskReduce my operating expenses through

automation, shared

capabilities and self service

Operational Efficiency

Protect my organization and

my customers from financial crimes while allowing my

business to grow

FraudRisk

Management


The cost of fraudulent applications

Source: 1Goldman Sachs 2Forrester Research: eCommerce Fraud Management Solutions 2014, Aite Group 2015

is expected to rise to

$28.6 billion by 20161



Explosion of new productsand channels How to monitor all of these, in real-time?

Risk management complexities: fraud and compliance monitoringHow to get a complete view of customer with limited information?

Originations Dynamics: Growing Need for Application Fraud Monitoring

Rising costof fraud and compliance monitoring How to improve a company’s ability to prevent bad people from entering the books and to know when a good customer goes bad?



Application Fraud Defined

An attempted or actual misrepresentation or manipulation of customer demographic (employment, residency, financial, etc.) in order to obtain a product or service that would otherwise not have been granted. This may be performed by the actual applicant,or by someone falsely representing themselves as the applicant.


Existing,Re-size

Applicant

New,Not Seen

New,Solicited

Existing,Extension

Existing,Solicited

New,Existing

Connection

Types of Applicant and Potential Fraud Risk

SocialEngineering

FalseIncome

Declaration

Early LifeDefaults


Existing,Re-size

Applicant

New,Not Seen

New,Solicited

Existing,Extension

Existing,Solicited

New,Existing

Connection

SocialEngineering

FalseIncome

Declaration

Early LifeDefaults

Types of Applicant and Potential Fraud Risk

Online

Online Mobile

In Branch

Online Social


Application Fraud Typologies

First-party fraud in credit cards worldwidecost $18.5BN in 2012 andwill rise to $28.6BNby 2016

Fraudulent obtaining of credit (often by falsifying information) without intending to pay it back

FIRST PARTY

Involves identity theft – Refers to fraud that is committed without the knowledge of a person whose identity is used to commit the fraud

THIRD PARTY


Addressing the Full Spectrum of Application Fraud

Existing, solicitedNew, not seen New, solicited New, existing connection Existing, extensionExisting, re-size

Early life defaults Bust out Identity theft False Income Declaration Social Engineering

ThirdParty

THREAT BUSINESS

FirstParty

Channel

Product

SourceSegme

nt

ActionAutomation

Investigation

DATA


FICO® Application Fraud Manager Product Overview


FICO® Application Fraud ManagerIdentify Both First and Third Party Application Fraud

Capability Benefit

Decisioning Service

• Single system for multiple channels and product lines• Matching engine / consortium-driven decisioning• Compares current application to other applications, fraud file, and credit

bureau records• Multiple-model execution• Real-time or batch

Analytics and Rules

• Leverage FICO-brand premium analytics and subject matter expertise that provide rapid deployment

• Modify and deploy rules in real-time for up-to-minute decisioning and fraud detection

Case Management

• Unique design helps analysts to zero in on specific areas• Investigative analysis, reporting, queue management• Integration with IRE for Link analysis with graphical view of systemic fraud

problems

Data Acquisition

Analytics

Decisioning

Case Management

Link Analysis


AFM System Components

Orchestration • The system employs an orchestration layer to coordinate data flow

between services• Can be configured to orchestrate data import from multiple sources• Configurable sequencing of analytics and rules

Decisioning• Based on Blaze Advisor system• Web-based configurable Rules Management Administration (RMA)• Develop, test and deploy rules using business-friendly environment

Case Management• Provides flexibility and adaptation to specific application types and

business processes• Standard web services interface permits utilization of alternate

system if desired• Link Analysis• Identifies potential linkages between current application entities and

other applications• Provides graphical investigation “discovery”

System Services

OrchestrationAnalytics

LinkAnalysis

Data

Rules

CaseMgmt.

Applications +Bureau Data Cases

Consortium


Application Fraud Analytics

1st and 3rd Party

3rd Party

CreditLine

PersonalLoans

AutoLoans

Mortgage

MobilePhone

InsurancePolicy

1st Party

Credit Card / Revolving

Credit

Consumer Loans

Insurance Policy

Mobile Phone

Auto Loans

Credit Card / Revolving

Credit

Consumer Loans

Insurance Policy

Mobile Phone

Auto Loans

Fraud Tags no Type Definition OR

Insufficient data

Tagged Fraud Data with Type Distinctions OR

Agreed Fraud Definition

Application Segmentsby Product andLine of Business


Models

PerformanceData

Historic Data forObservation Data

Key Elements to Building a Fraud Model

Application • Age, gender, address, contact info• Occupation

Applicant • Business, consumer, self employed

Bureau • Time at address, time at employer• Payments, delinquency

Product • Cards, loans, mortgages, vehicle financing• Retail/checking/current accounts

Authentication • ID type and number• Documentation

Customer • Time with bank• Other accounts and payment history

Industry Files • Industry negative files

Third Party Data • Deceased register, sanction list, phone number lookups, address changes


0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000

0

5

10

15

20

25

30 Fraud to Non-fraud Score Separation

Better Fraud Predictions Yield Better Business ResultsOperationalize Scores on Every Transaction

While minimizingthe false-positives

Advanced Analytics

increases the concentration

of fraud relative to

good transactions at high score

thresholdsWhile

minimizingthe false-negatives

Advanced Analytics

decreases the concentration

of fraud relative to

good transactions

at lower score thresholds

%

% Goods% Bads

Score Cut Off


Business Rules – Matching

• Matching rules are used to search the applications in the underlying application database for ones that contains fields that match one or more fields on the current application

• Types of matchinclude:─ Exact─ Fuzzy─ Wildcard─ Range


Value Proposition: AFM + IRE + Global ExpertiseFICO’s Application Fraud Solution can detect more fraud, more efficiently at the point of application before losses are incurred and customers are impacted

DetectionFind More Fraud

Fraud RingsDetect Collusion using SNA

AccuracyDecrease

False Positives

Customer Experience

Say “Yes” More

EfficiencyPrioritize Work

FutureExpansion

Expand Geographically,Horizontal and Vertical

Integration


FICO Application Fraud Solution Differentiators

Superior Analytics

FICO Models, SNA, Rules and Fuzzy Logic Detect

More Fraud with Lower False Positives

ModularDesign

Designed to Meet theUnique Needs of

Each Market

Pre-andPost-BookProvides Pre- and

Post-Book Detection and Investigative Tools

Social Network Analysis

FICO Provides SNAScoring of Applications

in Real-Time

Global Fraud Expertise

Industry-leading IP, GlobalFraud Expertise, Consulting

and Deployment

Flexibleand Scalable

FICO Provides Superior Decisioning, Flexibility

and Scalability

© 2015 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.

Thank You

Cindy White

© 2015 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.

Senior Director Product [email protected]

www.fico.com/enterprisefraud

Data & Analytics

FICO® Application Fraud Solution