Seshika Fernando

Technical Lead

Catch them in the Act Fraud Detection in Real-time

Fraud: A Trillion Dollar Problem

Survey results

๏ $ 3.5 – 4 Trillion in Global Losses per year

(5% of Global GDP)

Payment Fraud Only

๏ Merchants are losing around $ 250B globally

๏ Cost of Fraud is around 0.68% of Revenue for Retailers (2014)

๏ Steep rise in Fraud in eCommerce (0.85% of Revenue) and mCommerce (1.36% of Revenue) with a movement of payments to newer channels

3

Why WSO2 Analytics Platform?

Domain

Knowledge

Batch

Analytics

Interactive

Analytics

Real-time

Analytics

Predictive

Analytics

Fraud Detection Toolkit

Solution: Many Ways

Fraud = Anomaly

We provide many methods of Anomaly Detection in order to capture known and unknown

types of fraudulent behavior

๏ Generic Rules

๏ Fraud Scoring

๏ Advanced Techniques Capturing anomalous behavior using mathematical modelling

5

Capturing Domain Expertise

An example from Payment Fraud Domain

Fraudsters…

๏ Use stolen cards

๏ Buy Expensive stuff

๏ In Large Quantities

๏ Very quickly

๏ At odd hours

๏ Ship to many places

๏ Provide weird email addresses

CEP Queries

Generic Rules

Convert all pre-existing knowledge about Fraudulent Behavior within a domain to Generic Rules

๏ Blacklists/Whitelists

๏ Moving Averages

๏ Known Patterns

๏ Outliers

7

Queries for Expensive Purchases

define table PremiumProducts (itemNo string);

from TransactionStream[(itemNo== PremiumProducts.itemNo) in PremiumProducts ]

select *

insert into FraudStream;

8

Queries for Large Quantities

define table QuantityAverages

(itemNo string, avgQty int, stdevQty int);

from TransactionStream

[(itemNo== av.itemNo and qty > (av.avgQty + 3 * av.stdevQty)) in QuantityAverages as av]

select *

insert into FraudStream;

9

Queries for Large Quantities (Learning)

define table QuantityAverages

(itemNo string, avgQty int, stdevQty int);

from TransactionStream#window.time(8 hours)

select itemNo, avg(qty) as avg, stdev(qty) as stdev

group by itemNo

update QuantityAverages as av

on itemNo == av.itemNo;

from TransactionStream

[(itemNo== av.itemNo and qty > (av.avgQty + 3 * av.stdevQty)) in QuantityAverages as av]

select *

insert into FraudStream;

10

Queries for Transaction Velocity

from e1 = TransactionStream ->

e2 = TransactionStream[e1.cardNo == e2.cardNo]

within 5 min

select e1.cardNo, e1.txnID, e2[0].txnID, e2[1].txnID, e2[2].txnID

insert into FraudStream;

2:20

11

The False Positive Trap

๏ So what if I buy Expensive stuff

๏ And why can’t I buy a lot

๏ Very Quickly

๏ At odd hours

๏ Ship to many places

Rich guy

Gift giver

Busy man

Night owl

Many girlfriends?

Blocking genuine customers could be counter productive and costly

12

Fraud Scoring

๏ Use combinations of rules

๏ Give weights to each rule

๏ Derive a single number that reflects many fraud indicators

๏ Use a threshold to reject transactions

๏ You just bought a Diamond Ring?

๏ You bought 20 Diamond Rings, in 15 minutes at 3am from a blacklisted IP address?

13

Fraud Scoring

Score =

0.001 * itemPrice

+ 0.1 * itemQuantity

+ 2.5 * isFreeEmail

+ 5 * riskyCountry

+ 8 * suspicousIPRange

+ 5 * suspicousUsername

+ 3 * highTransactionVelocity

2:27

Learn from Data

Utilize Machine Learning Techniques to identify ‘unknown’ point anomalies

K-means Clustering

Use Markov Models to discover fraudulent behavior through rare activity sequences

Markov Models are stochastic models used to model randomly changing systems

15

Markov Models for Fraud Detection

16

Markov Modelling: Process

Classify Events Update

Probability Matrix

Compare Incoming

Sequences

Probability Matrix

Events Alerts

17

Markov Model: Classification

Example:

Each transaction is classified under the following three qualities and expressed as a 3 letter token, e.g., HNN

๏ Amount spent: Low, Normal and High

๏ Whether the transaction includes high price ticket item: Normal and High

๏ Time elapsed since the last transaction: Large, Normal and Small

18

๏ Create a State Transition Probability Matrix

Markov Models: Probability Matrix

LNL LNH LNS LHL HHL HHS HNS

LNL

0.976788 0.542152 0.20706 0.095459 0.007166 0.569172 0.335481

LNH

0.806876 0.609425 0.188628 0.651126 0.113801 0.630711 0.099825

LNS

0.07419 0.83973 0.951471 0.156532 0.12045 0.201713 0.970792

LHL

0.452885 0.634071 0.328956 0.786087 0.676753 0.063064 0.225353

HHL

0.386206 0.255719 0.451524 0.469597 0.810013 0.444638 0.612242

HHS

0.204606 0.832722 0.043194 0.459342 0.960486 0.796382 0.34544

HNS

0.757737 0.371359 0.326846 0.970243 0.771326 0.015835 0.574333

19

Markov Models: Probability Comparison

๏ Compare the probabilities of incoming transaction sequences with thresholds and flag fraud as appropriate

๏ Can use direct probabilities or more complex metrics ๏ Miss Rate Metric

๏ Miss Probability Metric

๏ Entropy Reduction Metric

๏ Update Markov Probability table with incoming transactions

2:35

Dig Deeper

Access historical data using

๏ expressive querying

๏ easy filtering

๏ useful visualizations

to isolate incidents and unearth connections

Usecase: Payment Fraud

Dashboard

Transactions

Transactions

Transactions

Transactions

Payment System

Batch Analytics

Interactive Analytics

Real-time Analytics

Predictive Analytics

Alerts

21

Usecase: Anti Money Laundering

Dashboard

Bank Txns

Bank Txns

Bank Txns

Bank Txns

Core Banking System

Batch Analytics

Interactive Analytics

Real-time Analytics

Predictive Analytics

Alerts

22

Usecase: Identity Fraud

Dashboard Events

Events

Batch Analytics

Interactive Analytics

Real-time Analytics

Predictive Analytics

Alerts

23 2:40

References

o WSO2 Whitepaper on Fraud Detection: http://wso2.com/whitepapers/fraud-detection-and-prevention-a-data-analytics-approach/

o True Cost of Fraud 2014 http://www.lexisnexis.com/risk/downloads/assets/true-cost-fraud-2014.pdf

o Stop Billions in Fraud Losses using Machine Learning https://www.forrester.com/Stop+Billions+In+Fraud+Losses+With+Machine+Learning/fulltext/-/E-res120912

o Big Data In Fraud Management: Variety Leads To Value And Improved Customer Experience https://www.forrester.com/Big+Data+In+Fraud+Management+Variety+Leads+To+Value+And+Improved+Customer+Experience/fulltext/-/E-RES103841

o Predictions 2015: Identity Management, Fraud Management, And Cybersecurity Converge https://www.forrester.com/Predictions+2015+Identity+Management+Fraud+Management+And+Cybersecurity+Converge/fulltext/-/E-RES120014

o Markov Modelling for Fraud Detection https://pkghosh.wordpress.com/2013/10/21/real-time-fraud-detection-with-sequence-mining/

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1/real-time-fraud-detection-with-sequence-mining/https://pkghosh.wordpress.com/2013/10/21/real-time-fraud-detection-with-sequence-mining/https://pkghosh.wordpress.com/2013/10/21/real-time-fraud-detection-with-sequence-mining/https://pkghosh.wordpress.com/2013/10/21/real-time-fraud-detection-with-sequence-mining/https://pkghosh.wordpress.com/2013/10/21/real-time-fraud-detection-with-sequence-mining/

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