PUTTING FRAUD TO THE FIRE

Transcription

1 PUTTING FRAUD TO THE FIRE Diana Kern, AAP senior trainer

2 AGENDA Fraud products and services Fraud statistics Fraud trends Compromised cards Reminders

3 FRAUD PRODUCTS & SERVICES

4 REDUCE FRAUD BY UTILIZING THESE PRODUCTS: Neural network case alerting Neural network real-time transaction scoring Blocks by transaction criteria Daily limits Direct cardholder contact via mobile application

5 NEURAL NETWORK CASE ALERTING Overview Doesn t decline or block Creates cases to be worked Issuer or third party contacts cardholder Scoring System scores all transactions as they batch into the neural network after authorization Transaction elements, cardholder history, consortium data

6 EXAMPLE RULES Scoring rule Create case if batch score is > XXX Card present rules Combat existing fraud trends Counterfeit activity Typically also real-time decline rule Card-not-present Combat existing fraud trends Internet fraud trends

7 NEURAL NETWORK REAL-TIME SCORING Scoring Scores, but uses fewer data elements than batch Score based on current and previously batched transactions What transactions are scored?

8 FALCON REAL-TIME RULES Score-based Will decline if exceeds threshold Specialty decline rules How do you get notified? How long are they in effect?

9 FALCON AND EMV Scoring Will score in a similar manner as magnetic stipe only card Signature vs. PIN Rules POS entry Chip card read = 5

10 BLOCKS BY TRANSACTION CRITERIA BIN level PAN level PAN exclusion Country code, merchant category code, merchant name, amount and other transaction fields

11 DAILY LIMITS Manageable for cardholders, but low enough to avoid large losses

12 MOBILE APPLICATION Cardholder alerts Internet, international, dollar amount Notification if new transaction activity that meets criteria occurs Neural network alerts

13 FRAUD STATISTICS

14 FRAUD STATISTICS SHAZAM FRAUD RATE TRENDING PIN Signature - NET SHAZAM Overall 0.08% 0.07% 0.06% 0.05% 0.04% 0.03% 0.02% 0.01% 0.00% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

15 FRAUD STATISTICS SHAZAM FRAUD REPORTING STATISTICS FOR % 1% 58% 39% Stolen Lost Counterfeit Acct Takeover Fraud Apps NRI CNP Acct. Takeover, Fraud Applications, & NRI = < 1%

16 FRAUD STATISTICS 2015 VISA 4,000,000,000 3,500,000,000 3,000,000,000 2,500,000,000 2,000,000,000 1,500,000,000 1,000,000, ,000,000 0 Lost Stolen NRI Fraud App Counterfeit Acct Takeover CNP US Worldwide

17 FRAUD STATISTICS 2015 MASTERCARD 3,000,000,000 2,500,000,000 2,000,000,000 1,500,000,000 1,000,000, ,000,000 0 Lost Stolen NRI Fraud App Counterfeit Acct Takeover CNP US Worldwide

18 FRAUD TRENDS

19 CARD NOT PRESENT Account testing Typically small dollar but not always Can be used as a test for daily limits Variety of merchants or, Hotel merchant names with a different merchant category code (MCC) Tests do not post Foreign Internet fraud Bangladesh Airlines Verified by Visa /SecureCode

20 COUNTERFEIT Increase in fraud reported as institutions/merchants migrate to EMV Using data from prior breaches before card reissue (Target,The Home Depot, etc.) Increase in foreign countries: Turkey, China, Honduras Increase in local skimming Pay at pump ATM EMV liability shift does not occur until 2017 Often results in local fraud

21 DOES THIS LOOK SUSPICIOUS?

22 SKIMMING DEVICE Photo of the cover Inspect all of your ATMs during opening and closing each day

23 SKIMMING DEVICE Inside the cover you can see the skimming parts

24 SKIMMING DEVICE Exterior shot of the ATM

25 ATM SKIMMING Regularly check terminals multiple times daily Look for signs of tampering Adhesive residue Skimmer devices Monitor outages Video View daily Position correctly Notify law enforcement if skimming is detected

26 PHISHING Increase in attempts to gain information PIN, CVV2, CVC2 Social engineering Microsoft scam results in PIN-based Internet fraud FI scam using EMV reissuance as part of scam IRS scam during tax time

27 WHAT TO DO WHEN YOU SEE A FRAUD TREND 3 or more PANs with similar fraud Is your neural network detecting it? Add a BIN level block based on transaction criteria Custom rule on neural network Custom query by processor

28 COMPROMISED CARDS

29 COMPROMISED CARDS MasterCard ADC-1234-US-16 Account data compromise Sequential alert number 4 digit Region in which compromise occurred Last two digits of year May designate sequential alerts

30 COMPROMISED CARDS MasterCard reimbursements MasterCard Account Data Compromise Recovery (ADCR) To maintain legal rights to join lawsuits, no reimbursements for magnetic stripe breach events in 2016 May still receive ADCR for prior years Reimbursements occur rarely Minimal recovery

31 COMPROMISED CARDS Visa US PA Region and year in which compromise occurred Sequential alert number 4 digits Type of alert PA proactive alert IC Internet compromise May designate sequential alerts

32 COMPROMISED CARDS Visa reimbursements Global Compromised Account Recovery (GCAR) May receive reimbursement for qualified events May receive offer to accept an additional recovery amount (ARO)

33 COMPROMISED CARDS Visa Reimbursements ARO Offer made by merchant whose data was compromised Target The Home Depot Offers are sent directly to Visa members based on BIN ownership Contain guaranteed Global Compromised Account Recovery (GCAR) amount Contain ARO amount If accepted, you lose any rights to sue or join class action lawsuits If refuse, you maintain legal rights and still receive GCAR amount FI responds directly to Visa

34 COMPROMISED CARDS HANDLING OPTIONS Hot card and reissue Call cardholder and advise Hot card or use temporary card block Could allow cards nearing expiration to reissue naturally Force reissue Risk until new card is received by cardholder and activated Cardholder can keep same PAN and PIN Expiration date and security values change so card is no longer at risk with old data

35 COMPROMISED CARDS HANDLING OPTIONS Cardholder control card proactively; mobile application Lower daily limits Neural network monitoring

36 COMPROMISED CARDS What data is at risk? What is window of exposure? Track data 1 and 2 puts card at risk for card present counterfeit fraud No chargeback rights Card Not Present PAN, expiration date, CVV2, CVC2 Risk for Internet or MO/TO fraud Potential chargeback rights Check window of exposure Determine if PAN on list has reissued after end date

37 COMPROMISED CARDS Recent alerts to be aware of Landry s MasterCard ADC-7231-US-15 Visa US PA Wendy s MasterCard ADC-0099-US-16 Visa US PA Hilton MasterCard ADC-6402-US-15 Visa US PA

38 REMINDERS Use on-hold time and website for cardholder education Take advantage of anti-fraud marketing materials available Keep daily limits low, but manageable Make sure after-hours recordings provide a 24/7 number to report fraud or lost/stolen cards Educate cardholders to use PIN when possible to avoid inconvenience

39