Fraud Analytics - Discussion


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Any business having unsecured revolving balances have to worry about potential fraud. Why? Fraud occurences typically show an inverse proportion to an organization's customer centricity, ie, more customer centricity higher the likelihhod of a fraud occurring.

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Fraud Analytics - Discussion

  1. 1. CONFIDENTIAL & LEGALLY PRIVILEGED Adiyanth Analytics Introduction to approaches in Fraud Analytics +91 888 494 madirajua
  2. 2. Page 2© adiyanth – Distribution Restricted Transaction data Application data Credit Bureau Data Use Diverse data Data Integration Generate Profiles Decision choices Develop Fraud 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.
  3. 3. Page 3© adiyanth – Distribution Restricted Indicative Outputs Analytical Models Decision Tools Systems Business Oriented Technology Oriented
  4. 4. Page 4© adiyanth – Distribution Restricted Fraud Analytics Credit Card Analytics Fraud Analytics as a Program - Economics - Driver Analysis - Competency
  5. 5. Page 5© adiyanth – Distribution Restricted Fraud Analytics Program – Three Gears Economics Driver Analysis Fraud Competency Developing Fraud Prevention Mechanisms • Financial Nature • Financial Cost/Benefit Determine the Levers & Leakages
  6. 6. Page 6© adiyanth – Distribution Restricted Economics – Fraud P & L Fraud * Credit Limit Good Balance Unused Utilization Fraud Exposure Detection 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)
  7. 7. Page 7© adiyanth – Distribution Restricted 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 cycle 2. Essential variables in decision process 3. Provide better documentation of business logic and enhance logic to improve maintainability Economics - Profitability Algorithm
  8. 8. Page 8© adiyanth – Distribution Restricted 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
  9. 9. Page 9© adiyanth – Distribution Restricted Fraud Analytics Program – Driver Analysis Economics Driver Analysis Fraud Competency Developing Fraud Prevention Mechanisms • Financial Nature • Financial Cost/Benefit Determine the Levers & Leakages
  10. 10. Page 10© adiyanth – Distribution Restricted Driver Analysis - Fraud Influencers Fraud $ Economic Conditions Cash Accessibility Marketing Shift Unsophisticated Business Intelligence Customer- centric Policies Shift towards sophisticated frauds
  11. 11. Page 11© adiyanth – Distribution Restricted 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
  12. 12. Page 12© adiyanth – Distribution Restricted Charge Offs – Variance Analysis & Forecasts Budget Higher YTD Incoming Stronger YTD Recovery 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
  13. 13. Page 13© adiyanth – Distribution Restricted Fraud Program - Competencies Economics Driver Analysis Fraud Competency Developing Fraud Prevention Mechanisms • Financial Nature • Financial Cost/Benefit Determine the Levers & Leakages
  14. 14. Page 14© adiyanth – Distribution Restricted 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
  15. 15. Page 15© adiyanth – Distribution Restricted Fraud Competency - Decision Tools 1. Statistical / Artificial Intelligence Based Models 1. 1st Payment Default Model – Score to identify fraudsters amongst the 1st payment defaulters 2. Early Behavior Models – Score to identify fraudsters based on the first 30 days of transactions 3. Internet Fraud Model – Score to identify potential fraud amongst e-shoppers 4. 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 Reports 1. Phone Zip Mismatch Report 2. High Risk ZipCode Report 3. Unusual Transaction Report 3. Industry Wide Infrastructural Mechanisms 1. Verisign 2. Staying Secure 3. MasterCard PayPass® 4. RiskWise
  16. 16. Page 16© adiyanth – Distribution Restricted 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
  17. 17. Page 17© adiyanth – Distribution Restricted Indicative Data Requirements I. Indicators of Need 1. Number of Tradelines 2. Utilization Rate 3. Missed Payments 4. Number of Enquiries 5. Utilities available on Name 6. Occupation 7. Number of Dependents 8. Marital Status 9. Income II. Indicators of Demand 1. No. of Rejected Applications 2. Number of transactions by high value SIC codes 3. Time Since last enquiry 4. Availability of co-applicant 5. Total unused credit limit III. Economic Indicators 1. Years at current employment 2. Years at the current residence 3. Monthly rental outgo 4. Monthly payments on utilities 5. Monthly credit card payments 6. Monthly mortgage payments 7. Total outstanding on unsecured credit IV. Discrepancies between application & Bureau data 1. Phone number Zip Code mismatch 2. Name & SSN mismatch 3. Invalid phone numbers 4. Address Mismatch 5. Employment Mismatch Micro Indicators – Credit Bureau Data Macro Indicators – Derived Characteristics
  18. 18. Page 18© adiyanth – Distribution Restricted 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
  19. 19. Page 19© adiyanth – Distribution Restricted 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