Fraudsters are smart, “Frank” is Smarter               - Fareed and Vivek28/01/13                            1
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)Thi...
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 t...
Getting specificReminder - 0.05% ratio is for all transactions including face to face transactionsThe fraud rate is a mu...
The Big Fight...Fraud to transaction ratio has been constant over the past 10 yearsThis ratio should not lull us into a ...
Why Frank?
Fraud Detection SystemTwo parts   Signals/Features   Algorithm                                 8 of 22
Rule based systemRules on various signals   Num of transaction from a card in last one day   Transaction amount   and ...
Need for Smarter system    Too much data for manual analysis    Businesses are evolving    Fraudsters are evolving    ...
Designing FrankLabeled data missingObservation   Very few fraud records   When you see one, you can identify oneSocia...
Visualization TotalAmount         Number of transactions in a day by a user                                               ...
Visualization                13 of 22
Clustering – Centroid based                              14 of 22
Clustering - Distance based                              15 of 22
Clustering - Distribution based                                  16 of 22
Clustering – Density based                             17 of 22
Density based clusteringEpsilonMin-pts                                      pStatistical distance                      ...
Clustering for detecting fraudCluster the data using density based clusteringFor new point find distance to all the exis...
Computing fraud probabilityWe find nearest clusterConvert the distance to probability   using chi-square distributionP...
ExecutionDistributed clusteringReal-time model updating< 20ms to compute fraud probabilitySuspend the payment authoriz...
We Frank, You Shop.                      22 of 22
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Slash n: Technical Session 7 - Fraudsters are smart, Frank is smarter - Vivek Mehta, Fareed Jawad

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  • Frank Abagnale Jr. First fraud before age of 16 Bank fraud, airline pilot, doctor and so on. 7-8 identities, fraud in 16 countries and got away from police custody twice, once from airplane. All this before age of 21. He was jailed for 5 years and helped FBI and then started his own company to help fraud prevention.
  • Slash n: Technical Session 7 - Fraudsters are smart, Frank is smarter - Vivek Mehta, Fareed Jawad

    1. 1. Fraudsters are smart, “Frank” is Smarter - Fareed and Vivek28/01/13 1
    2. 2. OutlineWhy detect fraud – Is there a problem?Why an intelligent system?How we built one 2 of 22
    3. 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. 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. 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. 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. 7. Why Frank?
    8. 8. Fraud Detection SystemTwo parts  Signals/Features  Algorithm 8 of 22
    9. 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 9 of 22
    10. 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 10 of 22
    11. 11. Designing FrankLabeled data missingObservation  Very few fraud records  When you see one, you can identify oneSocial behavior 11 of 22
    12. 12. Visualization TotalAmount Number of transactions in a day by a user 12 of 22
    13. 13. Visualization 13 of 22
    14. 14. Clustering – Centroid based 14 of 22
    15. 15. Clustering - Distance based 15 of 22
    16. 16. Clustering - Distribution based 16 of 22
    17. 17. Clustering – Density based 17 of 22
    18. 18. Density based clusteringEpsilonMin-pts pStatistical distance p1  Scale invariant q  Correlation taken into account 18 of 22
    19. 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 doesnt belong to any cluster -> fraud 19 of 22
    20. 20. Computing fraud probabilityWe find nearest clusterConvert the distance to probability  using chi-square distributionProbability of fraud between 0 and 1 20 of 22
    21. 21. ExecutionDistributed clusteringReal-time model updating< 20ms to compute fraud probabilitySuspend the payment authorization in real time 21 of 22
    22. 22. We Frank, You Shop. 22 of 22

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