Slash n: Technical Session 7 - Fraudsters are smart, Frank is smarter - Vivek Mehta, Fareed Jawad

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Fraudsters are smart, “Frank” is Smarter

- Fareed and Vivek

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Outline

Why detect fraud – Is there a problem?Why an intelligent system?How we built one

Show me some numbers

What was the value of all electronic transactions globally for year 2012?

$17 trillion (with a T)This includes all credit, debit and pre-paid

cards used in both online and offline (card present) scenarios for purchases and cash withdrawals

More Numbers

How much of $17T was lost due to FRAUD?$8 billion in 2012, > $10 billion by 2015 Fraud rate of 0.05% – Not too bad right?Wrong !!

Getting specific

Reminder - 0.05% ratio is for all transactions including face to face transactions

The fraud rate is a much more scary 3.5% for Online transactions aka CNP

Global e-Commerce is expected to exceed $1T in 2013 –> $3.5B will be lost due to fraud

Add to this, the erosion due to loss of future business from impacted customers

Big Customer Impact ! Big deal for us !!

The Big Fight...

Fraud to transaction ratio has been constant over the past 10 years

This ratio should not lull us into a false sense of security – bigger numbers are at stake and increasing as volumes grow

The crooks LOVE e-Commerce (think 3.5%)How do we then figure out if a transaction is

genuine or a victim of fraudIntelligently of course! - ENTER FRANK !!

Why Frank?

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Fraud Detection System

Two partsSignals/FeaturesAlgorithm

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Rule based system

Rules on various signalsNum of transaction from a card in last one dayTransaction amountand many more

Thresholds are hand craftedFraud Score = sum of individual scores

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Need for Smarter system Too much data for manual analysis Businesses are evolving Fraudsters are evolving Extending to really high dimension – pushing

beyond limits of rule based system

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Designing Frank

Labeled data missingObservation

Very few fraud records

When you see one, you can identify one

Social behavior

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Visualization

Number of transactions in a day by a user

Total Amount

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Visualization

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Clustering – Centroid based

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Clustering - Distance based

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Clustering - Distribution based

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Clustering – Density based

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Density based clustering

p

qp1

EpsilonMin-ptsStatistical distance

Scale invariantCorrelation taken into

account

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Clustering for detecting fraud

Cluster the data using density based clusteringFor new point find distance to all the existing

clustersIf there exists min-pts with epsilon dist in a

cluster, new point belongs to this clusterIf doesn't belong to any cluster -> fraud

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Computing fraud probability

We find nearest clusterConvert the distance to probability

using chi-square distribution

Probability of fraud between 0 and 1

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Execution

Distributed clusteringReal-time model updating< 20ms to compute fraud probabilitySuspend the payment authorization in real

time

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We Frank, You Shop.