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WSO2Con EU 2015: Catch Them in the Act: Fraud Detection With WSO2 Analytics Platform

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WSO2Con EU 2015: Catch Them in the Act: Fraud Detection With WSO2 Analytics Platform

Presenter:
Seshika Fernando
Technical Lead,
WSO2

Published in: Marketing
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WSO2Con EU 2015: Catch Them in the Act: Fraud Detection With WSO2 Analytics Platform

  1. 1. Catch  them  in  the  Act   Fraud  Detec+on  with     WSO2  Analy+cs  Pla:orm   Seshika  Fernando   Technical  Lead   WSO2    
  2. 2. Bad  News   $4 Trillion in Global Fraud Losses   That’s  5%  of  Global  GDP  
  3. 3. Good  News   WSO2  Analy9cs  Pla;orm  +  Domain  Exper9se  ≈  Digital  Sherlock    
  4. 4. Domain  Exper+se          Generic  Rules        
  5. 5. Typical  Fraudster   •  Use  stolen  cards   •  Buy  Expensive  stuff   •  In  Large  Quan++es   •  Very  quickly   •  At  odd  hours   •  Ship  to  many  places   •  Get  rejected  oSen   Siddhi  Queries  
  6. 6.  from    e1  =  Transac+onStream  -­‐>        e2  =  Transac+onStream[e1.cardNo  ==  e2.cardNo]  <2:>    within  5  min    select  e1.cardNo,    e1.txnID,    e2[0].txnID,    e2[1].txnID    insert  into  FraudStream   Transac9on  Velocity  
  7. 7. The  False  Posi9ve  Trap   Rich  guy   GiS  giver   Impulse  Shopper   Night  owl   Many  girlfriends?     ๏  So  what  if  I  buy  Expensive  stuff     ๏  And  why  can’t  I  buy  a  lot     ๏  Very  Quickly     ๏  At  odd  hours     ๏  Ship  to  many  places     Blocking  genuine  customers  could  be  counter   produc9ve  and  costly  
  8. 8. How  to  avoid  False  Posi9ves   •  Use  combina+ons  of  rules   •  Give  weights  to  each  rule   •  Single  number  that  reflects  many  fraud  indicators   •  Use  a  threshold  to  reject  transac+ons   •  You  just  bought  a  Diamond  Ring?     •  You  bought  20  Diamond  Rings,  in  15  minutes   at  3am  from  an  IP  address  in  Nigeria?    
  9. 9. How  to  score   Score  =        0.001  *  itemPrice        +    0.1    *  itemQuan+ty          +    2.5    *  isFreeEmail          +    5    *  riskyCountry                    +    8    *  suspicousIPRange          +    5    *  suspicousUsername      +    3  *  highTransac+onVelocity      
  10. 10.     Are  we  safe  ?  
  11. 11. Markov  Models   •  Model  randomly  changing  systems   •  Detect  rare  ac+vity  sequences  using   •  Classifica+on   •  Probability  Calcula+on   •  Metric  Calcula+on  
  12. 12. Fraud  Detec9on  in  Real-­‐9me   •  Encode  Domain  Knowledge  into  Generic  Rules   •  Use  Fraud  Scoring  to  reduce  False  Posi+ves   •  Use  Markov  Modelling  to  detect  rare  pakerns  
  13. 13. Have we cracked the case?
  14. 14. Dig  using  Big   •  Provide  access  to  historical  data  to  dig  deeper   •  Make  querying  and  filtering  easy  and  intui+ve     •  Provide  useful  visualiza+ons  to  isolate   incidents  and  unearth  connec+ons  
  15. 15. Dashboard   Fraud  Detec9on  Toolkit   Events   Events   Events   Events  
  16. 16. Payment  Fraud   Dashboard   Transac+ons   Transac+ons   Transac+ons   Transac+ons   Payment   System  
  17. 17. An9  Money  Laundering   Dashboard   Bank  Txns   Bank  Txns   Bank  Txns   Bank  Txns   Core   Banking   System  
  18. 18. Iden9ty  Fraud   Dashboard   Events   Events   Events   Events   WSO2   Iden+ty   Server  
  19. 19. Thank  You  

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