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Catch	
  them	
  in	
  the	
  Act	
  
Fraud	
  Detec+on	
  with	
  	
  
WSO2	
  Analy+cs	
  Pla:orm	
  
Seshika	
  Fernando	
  
Technical	
  Lead	
  
WSO2	
  
	
  
Bad	
  News	
  
$4 Trillion in Global Fraud Losses	
  
That’s	
  5%	
  of	
  Global	
  GDP	
  
Good	
  News	
  
WSO2	
  Analy9cs	
  Pla;orm	
  +	
  Domain	
  Exper9se	
  ≈	
  Digital	
  Sherlock	
  
	
  
Domain	
  Exper+se	
   	
  	
  	
  	
  Generic	
  Rules	
  
	
  
	
  
	
  
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	
  
 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	
  
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	
  
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?	
  	
  
How	
  to	
  score	
  
Score	
  =	
  	
  
	
  	
  0.001	
  *	
  itemPrice	
  	
  
	
  	
  +	
  	
  0.1	
  	
  *	
  itemQuan+ty	
   	
  	
  
	
  	
  +	
  	
  2.5	
  	
  *	
  isFreeEmail	
   	
  	
  
	
  	
  +	
  	
  5	
   	
  *	
  riskyCountry	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  +	
  	
  8	
   	
  *	
  suspicousIPRange	
   	
  	
  
	
  	
  +	
  	
  5	
   	
  *	
  suspicousUsername	
  
	
  	
  +	
  	
  3 	
  *	
  highTransac+onVelocity	
  
	
  	
  
 	
  
Are	
  we	
  safe	
  ?	
  
Markov	
  Models	
  
•  Model	
  randomly	
  changing	
  systems	
  
•  Detect	
  rare	
  ac+vity	
  sequences	
  using	
  
•  Classifica+on	
  
•  Probability	
  Calcula+on	
  
•  Metric	
  Calcula+on	
  
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	
  
Have we
cracked
the case?
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	
  
Dashboard	
  
Fraud	
  Detec9on	
  Toolkit	
  
Events	
  
Events	
  
Events	
  
Events	
  
Payment	
  Fraud	
  
Dashboard	
  
Transac+ons	
  
Transac+ons	
  
Transac+ons	
  
Transac+ons	
  
Payment	
  
System	
  
An9	
  Money	
  Laundering	
  
Dashboard	
  
Bank	
  Txns	
  
Bank	
  Txns	
  
Bank	
  Txns	
  
Bank	
  Txns	
  
Core	
  
Banking	
  
System	
  
Iden9ty	
  Fraud	
  
Dashboard	
  
Events	
  
Events	
  
Events	
  
Events	
  
WSO2	
  
Iden+ty	
  
Server	
  
Thank	
  You	
  

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

  • 1. Catch  them  in  the  Act   Fraud  Detec+on  with     WSO2  Analy+cs  Pla:orm   Seshika  Fernando   Technical  Lead   WSO2    
  • 2. Bad  News   $4 Trillion in Global Fraud Losses   That’s  5%  of  Global  GDP  
  • 3. Good  News   WSO2  Analy9cs  Pla;orm  +  Domain  Exper9se  ≈  Digital  Sherlock    
  • 4. Domain  Exper+se          Generic  Rules        
  • 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.  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. 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. 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. How  to  score   Score  =        0.001  *  itemPrice        +    0.1    *  itemQuan+ty          +    2.5    *  isFreeEmail          +    5    *  riskyCountry                    +    8    *  suspicousIPRange          +    5    *  suspicousUsername      +    3  *  highTransac+onVelocity      
  • 10.     Are  we  safe  ?  
  • 11. Markov  Models   •  Model  randomly  changing  systems   •  Detect  rare  ac+vity  sequences  using   •  Classifica+on   •  Probability  Calcula+on   •  Metric  Calcula+on  
  • 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  
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  • 17. 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  
  • 18. Dashboard   Fraud  Detec9on  Toolkit   Events   Events   Events   Events  
  • 19. Payment  Fraud   Dashboard   Transac+ons   Transac+ons   Transac+ons   Transac+ons   Payment   System  
  • 20. An9  Money  Laundering   Dashboard   Bank  Txns   Bank  Txns   Bank  Txns   Bank  Txns   Core   Banking   System  
  • 21. Iden9ty  Fraud   Dashboard   Events   Events   Events   Events   WSO2   Iden+ty   Server  
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