Fraud Analytics - Discussion

CONFIDENTIAL & LEGALLY PRIVILEGED

Adiyanth AnalyticsIntroduction to approaches in Fraud Analytics

+91 888 494 [email protected] madirajua

© adiyanth – Distribution Restricted

Transaction data

Application data

Credit Bureau Data

Use Diverse data Data Integration

Generate Profiles

Decision choices

DevelopFraud Score

Indicative Approach

Reject application

Reduce Loan size

Restrict services offerings

Predictive analytic based tools are effective in identifying fraudulent trends before impact spreads. The analytical solution in addition to giving a Fraud score provides possible actions based customer related profiles.


Indicative Outputs

Analytical ModelsDecision

ToolsSystems

Business Oriented

Technology Oriented


Fraud Analytics

Credit Card Analytics

Fraud Analytics as a Program - Economics

- Driver Analysis - Competency


Fraud Analytics Program – Three Gears

Economics

Driver Analysis

Fraud Competency

Developing Fraud Prevention Mechanisms

• Financial Nature• Financial Cost/Benefit

Determine the Levers & Leakages


Economics – Fraud P & L

Fraud *

Credit LimitGood

BalanceUnused

UtilizationFraud

ExposureDetection Revenue

Incoming Fraud

Recovery Rev Charge

Backs

Recovery Rev

Rebills

Charge-off

Fraud Ops Revenue

Monthly MIS to track P&L components to enable strategy refinements on need basis

Managing Authorizations (Profitability Algorithms)


Decision Type Scenarios Considered

Approval • Fraud Recovery for Approved Fraud Transaction

Referral• Fraud Incurred from approval after customer calls back on

soft decline

• Fraud incurred when merchant calls back

• Fraud recovery for frauds approved on customer call-back or merchant call-back

Decline• Fraud incurred from approval after customer calls back

• Fraud Recovery for frauds approved on customer call back

1. Provide complete consideration of authorization decision life cycle2. Essential variables in decision process3. Provide better documentation of business logic and enhance logic to improve

maintainability

Economics - Profitability Algorithm


Economics – Usage of Profitability Algorithm within Approval Decision

Fraud?

Approval

Good Transaction= (1-p(Fraud)* Trans_amt*

rate of return)

Collect?=-(p(Fraud) * cost

of recoveries

Charge off= p(Fraud)*C/O rate*Tran amt

Collected Frauds=(p(Frauds)*(1-C/O rate)*Tran amt*Rate of Return

Yes No

Yes No


Fraud Analytics Program – Driver Analysis

Economics

Driver Analysis

Fraud Competency





Driver Analysis - Fraud Influencers

Fraud $

Economic Conditions

Cash Accessibility

Marketing Shift

Unsophisticated Business

Intelligence

Customer-centric Policies

Shift towards sophisticated

frauds


Driver Analysis – Pyramid Framework

L4 – Criticality of

Drivers

L1 – Macro Drivers

L3 – Relative

Importance of Drivers

L2 – Drivers of Macro Drivers

Outcomes Measurements

Impacts

Leading to

a. Pro-active Prevention• Authorization rules @ SIC code level• Preferred activation transactions• Identifying unusual transactions

b. Reactive Detection Reports


Charge Offs – Variance Analysis & Forecasts

Budget Higher YTD Incoming

Stronger YTDRecovery

Performance

YTD Variance

Fraud Rings/NRI

Driver Variance

Case Reforecast

Underlying Increase in Incoming

Fraud

Jan Forecast

Variance analysis of Charge Off - Budget vs Actual


Fraud Program - Competencies

Economics

Driver Analysis

Fraud Competency





Fraud Competency – Keeping Fraudsters at Bay

Proactive, Broad-Based Fraud Competency

Technological Sophistication

Focus on Prevention

Focus on Detection

• New Defense Architecture• Rule Engine Expansion• Cutting Edge Platforms

• Focus on Contribution• Bench Marking• Cutting-Edge Decision Tools

• Deep Dive LOB Analysis• Targeted Processes - Exposure• LOB Partnerships


Fraud Competency - Decision Tools

1. Statistical / Artificial Intelligence Based Models1. 1st Payment Default Model – Score to identify fraudsters amongst the 1st payment

defaulters2. Early Behavior Models – Score to identify fraudsters based on the first 30 days of

transactions3. Internet Fraud Model – Score to identify potential fraud amongst e-shoppers4. Probability of Charge-off Model – Score to identify fraud account likely to go charge-off 5. Probability of Fraud Model – Score to identify the prospect likely to be fraud

2. Ad-hoc Fraud Behavior Reports1. Phone Zip Mismatch Report2. High Risk ZipCode Report3. Unusual Transaction Report

3. Industry Wide Infrastructural Mechanisms1. Verisign2. Staying Secure3. MasterCard PayPass®4. RiskWise


Identity Fraud Model will have 3 attributes

Indicators of Identity Mis-Match• High Risk Zip Codes • Invalid Phone Numbers• Incomplete application forms

Indicators of Profile Mis-Match • Differences in information available from Credit Bureau and

Application • High Risk Occupations• Phone number & City Mis-match

Usage of High-Risk Channels• Prefer online applications with instant credit access• Multiple applications within short span• Frequent Lost & Stolen cases registered


Indicative Data Requirements

I. Indicators of Need

1. Number of Tradelines2. Utilization Rate3. Missed Payments4. Number of Enquiries5. Utilities available on Name6. Occupation7. Number of Dependents8. Marital Status9. Income

II. Indicators of Demand

1. No. of Rejected Applications2. Number of transactions by high value

SIC codes3. Time Since last enquiry4. Availability of co-applicant5. Total unused credit limit

III. Economic Indicators

1. Years at current employment2. Years at the current residence3. Monthly rental outgo4. Monthly payments on utilities5. Monthly credit card payments6. Monthly mortgage payments7. Total outstanding on unsecured credit

IV. Discrepancies between application & Bureau data

1. Phone number Zip Code mismatch2. Name & SSN mismatch3. Invalid phone numbers4. Address Mismatch5. Employment Mismatch

Micro Indicators – Credit Bureau Data

Macro Indicators – Derived Characteristics


Key Milestones during Model Build

• Run the driver list through various

statistical / machine learning algorithms

to establish criticality

• Based on the goodness-of-fit the final algorithm & candidate model is selected

• Identify all the potential drivers and segment them into fraud influencers as discussed earlier

• Defining the candidate fraud behaviors

• Evaluating the impacts of each behavior Defining

Potential Fraudulent Behavior

Creating Potential Driver List

Establishing criticality of each driver

Establishing the weightings for each driver

& assigning the final score

Back to Credit Card Analytics

Campaign Management Solutions


Adiyanth Analytics is being set up with a vision of supplying analytical capabilities to organizations that would want to "compete and win" based on its Data-driven Competitive Advantage. We intend to arm the clients with this capability through any one of the 3 core approaches - Outsourcing, Data Solutions, Professional Services. We focus on Information & Knowledge management services wishing to cater to market segment that consists of :

organizations that have experienced growth for at least 5 years resulting in a unique culture, brand recall, appreciation and market expectations being at their peak.

These organizations are now at cusp and are at risk of quickly slipping into “trough of disillusionment”, or at best feared for, flattened slope of enlightenment from any misstep.

They are addressing the 3 key challenges of Information Economy, viz., Availability, Accessibility & Affordability of "knowledge for decision making"

About Us

Fraud Analytics - Discussion

Business

Transcript of Fraud Analytics - Discussion