1. Fraudsters are smart, “Frank” is Smarter
- Fareed and Vivek
28/01/13 1

2. Outline
Why detect fraud – Is there a problem?
Why an intelligent system?
How we built one
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3. 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

4. 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 !!

5. 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 !!

6. 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 fraud
Intelligently of course! - ENTER FRANK !!

7. Why Frank?

8. Fraud Detection System
Two parts
 Signals/Features
 Algorithm
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9. Rule based system
Rules on various signals
 Num of transaction from a card in last one day
 Transaction amount
 and many more
Thresholds are hand crafted
Fraud Score = sum of individual scores
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10. 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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11. Designing Frank
Labeled data missing
Observation
 Very few fraud records
 When you see one, you can identify one
Social behavior
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12. Visualization
Total
Amount
Number of transactions in a day by a user
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13. Visualization
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14. Clustering – Centroid based
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15. Clustering - Distance based
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16. Clustering - Distribution based
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17. Clustering – Density based
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18. Density based clustering
Epsilon
Min-pts
p
Statistical distance
p1
 Scale invariant q
 Correlation taken into
account
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19. Clustering for detecting fraud
Cluster the data using density based clustering
For new point find distance to all the existing
clusters
If there exists min-pts with epsilon dist in a
cluster, new point belongs to this cluster
If doesn't belong to any cluster -> fraud
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20. Computing fraud probability
We find nearest cluster
Convert the distance to probability
 using chi-square distribution
Probability of fraud between 0 and 1
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21. Execution
Distributed clustering
Real-time model updating
< 20ms to compute fraud probability
Suspend the payment authorization in real
time
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22. We Frank, You Shop.
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