fraud detection with analytics wso2 asiacon2016
TRANSCRIPT
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Catch them in the Act Fraud Detection with WSO2 Analytics Platform
Seshika Fernando Technical Lead WSO2
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$4 Trillion in Global Fraud Losses
Analysts predict Businesses are losing 5% of business revenues to Fraud each year
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Many Ways
• Generic Rules • Fraud Scoring • Machine Learning • Markov Models
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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
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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
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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
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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
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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
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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?
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How to score
Score = 0.001 * itemPrice
+ 0.1 * itemQuantity
+ 2.5 * isFreeEmail
+ 5 * riskyCountry
+ 8 * suspicousIPRange
+ 5 * suspicousUsername
+ 3 * highTransactionVelocity
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Known devil is better than an unknown angel...
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Machine Learning
Utilize Machine Learning techniques to identify ‘unknown’ types of fraud
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Is organized crime that simple?
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Markov Models
• Model randomly changing systems • Detect rare activity sequences using
– Classification – Probability Calculation – Metric Calculation
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Markov Models for Fraud Detection
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One true inference invariably suggests
others
- Sherlock Holmes
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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
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Curious?
http://wso2.com/analytics/solutions/fraud-and-anomaly-detection-solution/
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Payment Fraud
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Anti Money Laundering
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Identity Fraud
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Thank You