Application Fraud Detection Models

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Clement Oliver
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1 Application Fraud Detection Models Credit Scoring and Credit Control X, Edinburgh 2007

2 Hunter Overview Hunter Product that identifies applications with a high risk of fraud Rules are triggered as the current application matches against: Previous Applications Known Fraud Look Up Files Other Value Added Data Sources As more rules are triggered the probability of fraud increases Hunter is a Global Product Hunter originated in the UK set up by MCL Software Multiple large UK clients subscribe to Hunter Hunter is now available globally in countries such as Spain and Russia Why use Scoring within Hunter? Make use of full Hunter rule sets Are all referrals necessary? Prioritise applications with the highest risk

3 Rule Ranking Hunter Scoring 1: Development Sample Creation 2 : Hunter Rule Analysis Four client data samples collated Products covered Credit Cards / Loans / Mortgages Only keep applications that have been worked by a referral team Extract all rule hits for each application Assess Good to Fraud Odds and Good to Fraud Index for Each Rule Produce false positive rates for each rule that is triggered 3: Correlation Analysis Produce a correlation table for each client sample If rules are highly correlated consider only using 1 of the rules for the development sample to prevent over modelling 4: Rule Risk Band Creation Order all rule hits by good to fraud index Allocate risk bands by good to fraud index 5: Hunter Score Model Development Amalgamate client samples Coarse classing of rule hits within each risk band Run linear regression model to produce final scorecard

triggered 3: Correlation Analysis Produce a correlation table for each client sample If rules are highly correlated consider only using 1 of the rules for the development sample to prevent over

4 Rule Ranking Hunter Scoring Good / Fraud & Suspect Index Creation Create Good / Fraud & Suspect Odds for Population and for Each Rule Good / Fraud & Suspect Odds = Rule Odds / Population Odds * 100 No Rule Odds < Population Odds? Yes Good / Fraud & Suspect Odds = Population Odds / Rule Odds * 100 Probability of Fraud

Odds = Rule Odds / Population Odds * 100 No Rule Odds < Population Odds?

5 Rule Ranking Hunter Scoring Population Stats Apps Goods Frauds Suspects G/F&S Odds 222, ,920 4,595 7, Cut-off 1 - Auto Accept Cases with HUNTER score > Auto Accept 15% Accepted Apps % of Apps Goods Frauds Suspe cts G/F&S Odds 15.79% 34, Re ferred Apps % of Apps Goods Frauds Suspe cts G/F&S Odds % of Fraud Referred 84.21% 175,528 4,447 7, % Cut-off 2 - Auto Accept Cases with HUNTER score > Auto Accept 25% Accepted Apps % of Apps Goods Frauds Suspects G/F&S Odds 24.40% 53, Referred Apps % of Apps Goods Frauds Suspects G/F&S Odds % of Fraud Referred 75.60% 156,459 4,313 7, %

33 Re ferred Apps % of Apps Goods Frauds Suspe cts G/F&S Odds % of Fraud Referred 84.21% 175,528 4,447 7,440 14.77 96.

6 Rule Ranking Hunter Scoring Average Hunter Score for Client by Product Each client can compare the average Hunter score for each of their products Each client can compare the average Hunter score for each of their products This may result in them re-focussing their referral teams priorities Products Client1 Client2 Client3 Cards Loans Mortgages

score for each of their products This may result in them re-focussing their referral teams

7 Identity Fraud - Validation / Verification Matrices Why is it important to consider two areas when tackling Identity Fraud? Validation and Verification Validation = Is there any proof that the applicant exists at the address in question? Electronic Proof of Name at Address Verification = How likely is the applicant to actually be the person they are claiming to be? Use of additional corroborative characteristics No inconsistencies in key application data

Validation and Verification Validation = Is there any proof that the applicant exists at the address in question?

8 Identity Fraud - Validation / Verification Matrices Validation Scoring Proof based methodology Major level proof of name at current address points Minor level proof of name at current address + 50 points Major Data Proof Examples Active Credit Account Active Mortgage Credit Account Settled Credit Account County Court Judgement Registered On Voters Roll Minor Data Proof Example Home Phone Number Confirmed via BT OSIS Verification Scoring Positive Corroboration Scoring Counts of Matches for: Date of Birth Time at Address Home Telephone Number Supplied Previous Address Negative Scoring of Data Inconsistencies Detect Rule Triggers Identifying Mismatched Date of Births Mismatched Home Telephone Number Forwarding Address Link from Current Address On Voters Roll before Stated Occupancy

Number Confirmed via BT OSIS Verification Scoring Positive Corroboration Scoring Counts of Matches for: Date of Birth Time at Address Home Telephone Number Supplied Previous Address Negative Scoring

9 Identity Fraud - Validation / Verification Matrices High Risk Population Medium Risk Population

10 Identity Fraud - Validation / Verification Matrices Authentication Index Allocation Three or More Electronic Proofs of Data Confirmed Two or More Electronic Proofs of Data Confirmed One or More Electronic Proofs of Data Confirmed No Electronic Proofs of Data at Address or Significant Inconsistencies Found No Positive Authentication can be % of Applications Authenticated = 86% % of Known Identity Fraud Referred = 77%

or More Electronic Proofs of Data Confirmed No Electronic Proofs of Data at Address or Significant Inconsistencies

11 Summary Fraud is still costing the global economy billions of s every year The monitoring of new fraud scoring methods should be an on-going process adapting to new fraud perpetration techniques Scoring of risk banded rule counts helps to Prioritise referral cases Automate low risk decision making Validation and Verification elements work well in conjunction to prevent Identity Fraud Roll Out To Industry

Scoring of risk banded rule counts helps to Prioritise referral cases Automate low risk decision

Data sharing for Fraud Prevention in Medical Insurance

Data sharing for Fraud Prevention in Medical Insurance Nick Mothershaw, Director Identity and Fraud Experian Limited 2008. All rights reserved. Experian and the marks used herein are service marks or registered