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Fraud detection with Analytics wso2 asiacon2016

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This is the slide deck I used at my talk titled 'Catch them in the Act - Fraud Detection with WSO2 Analytics Platform' at the wso2 Asia Conference 2016

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Fraud detection with Analytics wso2 asiacon2016

  1. 1. Catch them in the Act Fraud Detection with WSO2 Analytics Platform Seshika Fernando Technical Lead WSO2
  2. 2. $4 Trillion in Global Fraud Losses Analysts predict Businesses are losing 5% of business revenues to Fraud each year
  3. 3. Many Ways • Generic Rules • Fraud Scoring • Machine Learning • Markov Models
  4. 4. Capturing Domain Expertise Fraudsters • Use stolen cards • Buy expensive stuff • In large quantities • Very quickly • At odd hours • Ship to many places • Provide weird email addresses
  5. 5. Complex Event Processing Notify if there is a 10% increase in overall trading activity AND the average price of commodities has fallen 2% in the last 4 hours
  6. 6. Moving Averages from TransactionStream#window.time(60 min) select itemNo, avg(qty), stdev(qty) group by itemNo update AvgTbl as a on itemNo == a.itemNo from TransactionStream[itemNo == a.itemNo and qty>(a.avg +3*a.stdev) in AvgTbl as a] select * insert into FraudStream
  7. 7. Transaction Velocity from e1 = TransactionStream -> e2 = TransactionStream[e1.cardNo==e2.cardNo]<2:> within 5 min select e1.cardNo, e1.txnID, e2[0].txnID, e2[1].txnID insert into FraudStream
  8. 8. The False Positive Trap So what if I buy expensive stuff Very quickly At odd hours Ship to many places Rich guy Impulse Shopper Night owl Many girlfriends? Blocking genuine customers could be counterproductive and costly
  9. 9. Avoid False Positives with Scoring Use combination of rules Give weights to each rule Single number that reflects multiple fraud indicators Use a threshold to reject transactions • You just bought a diamond ring • You bought 20 diamond rings, within 15 minutes at 3am and shipped it to 4 global locations?
  10. 10. How to score Score = 0.001 * itemPrice + 0.1 * itemQuantity + 2.5 * isFreeEmail + 5 * riskyCountry + 8 * suspicousIPRange + 5 * suspicousUsername + 3 * highTransactionVelocity
  11. 11. Known devil is better than an unknown angel...
  12. 12. Machine Learning Utilize Machine Learning techniques to identify ‘unknown’ types of fraud
  13. 13. Is organized crime that simple?
  14. 14. Markov Models • Model randomly changing systems • Detect rare activity sequences using – Classification – Probability Calculation – Metric Calculation
  15. 15. Markov Models for Fraud Detection
  16. 16. One true inference invariably suggests others - Sherlock Holmes
  17. 17. Dig deeper using Interactive Analytics • Provide access to historical data to dig deeper • Make querying and filtering easy and intuitive • Provide useful visualizations to isolate incidents and unearth connections
  18. 18. Curious? http://wso2.com/analytics/solutions/fraud-and- anomaly-detection-solution/
  19. 19. Payment Fraud
  20. 20. Anti Money Laundering
  21. 21. Identity Fraud
  22. 22. Thank You

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