Fraud Analytics

Fraud Analytics

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  Easy Solutions
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  About us Industry recognitionAleading global provider of electronic fraud prevention for financial institutions and enterprise customers 280+ customers In 26 countries 75 million Users protected 22+ billion Online connections monitored in last 12 months 2
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  Some of ourCustomers 3
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  Our Approach:Total FraudProtection® 4
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  Fraud Analytics Alejandro CorreaBahnsen, PhD Data Scientist
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  About me • PhDin Machine Learning at Luxembourg University • Data Scientist at Easy Solutions • Worked for +8 years as a data scientist at GE Money, Scotiabank and SIX Financial Services • Bachelor and Master in Industrial Engineering • Organizer of Data Science Luxembourg and recently of Big Data Science Bogota 6
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  ~1Billion USD ~171Millions USD ~3BillionsUSD Does fraud affect me? 7
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  € - € 100 €200 € 300 € 400 € 500 € 600 € 700 € 800 2007 2008 2009 2010 2011 2012 Europe fraud evolution Card not present (Internet) transactions 8
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  $- $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 2001 2002 20032004 2005 2006 2007 2008 2009 2010 2011 2012 US fraud evolution Card not present (Internet) transactions 9
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  1.10% 1.30% 1.10% 0.90% 0.88% 0.87% 0.09%0.08% 0.08% 0.06% 0.05% 0.05% 2006 2007 2008 2009 2010 2011 Card Present vs. Card Not Present Fraud Rates Card Not Present Card Present 23.3 26.8 30.0 33.3 35.0 2009 2010 2011 2012 2013 US Online Banking Billions of Transactions 1.2 3.0 5.6 9.4 14.0 2009 2010 2011 2012 2013 US Mobile Banking Billions of Transactions 10
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  There is aneed for better fraud detection strategies 11
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  BigData? 13
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  “War is ninetypercent information” • Napoleon Bonaparte 14
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  Big data (DataScience) is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it... 17
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  BigData Analytics 19
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  BigData Analytics isthe use of methods and tools of Machine Learning and Artificial Intelligence with the objective making data- driven decisions 20
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  Fraud detection and prevention 21
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  Estimate the probabilityof a transaction being fraud based on analyzing customer patterns and recent fraudulent behavior Issues when constructing a fraud detection system: • Skewness of the data • Cost-sensitivity • Short time response of the system • Dimensionality of the search space • Feature preprocessing • Model selection 22 Credit card fraud detection
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  Network Fraud?? 23
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  • Larger Europeancard processing company • 2012 & 2013 card present transactions • 20MM Transactions • 40,000 Frauds • 0.467% Fraud rate • ~ 2MM EUR lost due to fraud on test dataset Dec Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan Test Train Data
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  • “Purpose isto use facts and rules, taken from the knowledge of many human experts, to help make decisions.” • Example of rules • More than 4 ATM transactions in one hour? • More than 2 transactions in 5 minutes? • Magnetic stripe transaction then internet transaction? 25 If-Then rules (Expert rules)
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  1.04% 31% 17% 22% Miss-cla Recall PrecisionF1-Score 26 If-Then rules (Expert rules)
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  Credit card frauddetection is a cost-sensitive problem. As the cost due to a false positive is different than the cost of a false negative. • False positives: When predicting a transaction as fraudulent, when in fact it is not a fraud, there is an administrative cost that is incurred by the financial institution. • False negatives: Failing to detect a fraud, the amount of that transaction is lost. Moreover, it is not enough to assume a constant cost difference between false positives and false negatives, as the amount of the transactions varies quite significantly. 27 Financial evaluation
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  Cost matrix 𝐶𝑜𝑠𝑡 𝑓𝑆 = 𝑖=1 𝑁 𝑦𝑖 𝑐𝑖 𝐶 𝑇𝑃 𝑖 + 1 − 𝑐𝑖 𝐶 𝐹𝑁 𝑖 + 1 − 𝑦𝑖 𝑐𝑖 𝐶 𝐹𝑃 𝑖 + 1 − 𝑐𝑖 𝐶 𝑇𝑁 𝑖 28 Actual Positive 𝒚𝒊 = 𝟏 Actual Negative 𝒚𝒊 = 𝟎 Predicted Positive 𝒄𝒊 = 𝟏 𝐶 𝑇𝑃 𝑖 = 𝐶 𝑎 𝐶 𝐹𝑃 𝑖 = 𝐶 𝑎 Predicted Negative 𝒄𝒊 = 𝟎 𝐶 𝐹𝑁 𝑖 = 𝐴𝑚𝑡𝑖 𝐶 𝑇𝑁 𝑖 = 0 Financial evaluation
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  1.24 € 1.94 € CostTotal Losses 1.04% 31% 17% 22% Miss-cla Recall Precision F1-Score 29 If-Then rules (Expert rules)
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  Fraud Analytics 30
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  Raw features 31 Attribute nameDescription Transaction ID Transaction identification number Time Date and time of the transaction Account number Identification number of the customer Card number Identification of the credit card Transaction type ie. Internet, ATM, POS, ... Entry mode ie. Chip and pin, magnetic stripe, ... Amount Amount of the transaction in Euros Merchant code Identification of the merchant type Merchant group Merchant group identification Country Country of trx Country 2 Country of residence Type of card ie. Visa debit, Mastercard, American Express... Gender Gender of the card holder Age Card holder age Bank Issuer bank of the card Features
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  Transaction aggregation strategy 32 RawFeatures TrxId Time Type Country Amt 1 1/1 18:20 POS Lux 250 2 1/1 20:35 POS Lux 400 3 1/1 22:30 ATM Lux 250 4 2/1 00:50 POS Ger 50 5 2/1 19:18 POS Ger 100 6 2/1 23:45 POS Ger 150 7 3/1 06:00 POS Lux 10 Aggregated Features No Trx last 24h Amt last 24h No Trx last 24h same type and country Amt last 24h same type and country 0 0 0 0 1 250 1 250 2 650 0 0 3 900 0 0 3 700 1 50 2 150 2 150 3 400 0 0 Features
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  When is acustomer expected to make a new transaction? Considering a von Mises distribution with a period of 24 hours such that 𝑃(𝑡𝑖𝑚𝑒) ~ 𝑣𝑜𝑛𝑚𝑖𝑠𝑒𝑠 𝜇, 𝜎 = 𝑒 𝜎𝑐𝑜𝑠(𝑡𝑖𝑚𝑒−𝜇) 2𝜋𝐼0 𝜎 where 𝝁 is the mean, 𝝈 is the standard deviation, and 𝑰 𝟎 is the Bessel function 33 Periodic features
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  34 Periodic features
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  Fraud Analytics isthe use of statistical and mathematical techniques (Machine Learning) to discover patterns in data in order to make predictions Fraud Analytics
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  Amountofthetransaction Number of transactionslast day Normal Transaction Fraud 36
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  37 Amountofthetransaction Number of transactionslast day Normal Transaction Fraud
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  38 Amount of thetransaction Normal Transaction Fraud Number of transactions last dayNumber of ATM transactions last week
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  Fraud Analytics Algorithms Fuzzy Rules NeuralNets Naive Bayes Random Forests Cost-Sensitive Random Patches Decision Trees 39
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  0% 20% 40% 60% 80% 100% Expert Rules FuzzyRules Neural Nets Naïve Bayes Random Forests CS Random Patches % Savings % Frauds 40
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  • Fraud Analytics(ML) models are significantly better than expert rules • Models should be evaluated taking into account real financial costs of the application • Algorithms should be developed to incorporate those financial costs Conclusions 41
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  Questions? Alejandro Correa Bahnsen,PhD Data Scientist acorrea@Easysol.net 42