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Catch	
  them	
  in	
  the	
  Act	
  
Fraud	
  Detec+on	
  in	
  Real-­‐+me	
  
Seshika	
  Fernando	
  
Technical	
  Lead	
  
WSO2	
  
	
  
Catch	
  them	
  
in	
  the	
  Act	
  
Fraud	
  Detec+on	
  
in	
  Real-­‐+me	
  
Seshika	
  Fernando	
  
Technical	
  Lead,	
  WSO2	
  
seshika@wso2.com	
  
	
  
$4 Trillion in Global Fraud Losses	
  
That’s	
  5%	
  of	
  Global	
  GDP	
  
Efficient	
  Fraud	
  Detec+on	
  
Domain	
  Exper+se	
  
Streaming	
  
Analy+cs	
  
Batch	
  
Analy+cs	
  
Predic+ve	
  
Analy+cs	
  
Complex	
  Event	
  Processing	
  
No#fy	
  if	
  there	
  is	
  a	
  10%	
  increase	
  in	
  overall	
  trading	
  ac#vity	
  AND	
  the	
  average	
  
price	
  of	
  commodi#es	
  has	
  fallen	
  2%	
  in	
  the	
  last	
  4	
  hours	
  
Many	
  ways	
  	
  
•  Generic	
  Rules	
  
•  Fraud	
  Scoring	
  
•  Markov	
  Models	
  
•  Machine	
  Learning	
  
Domain	
  Exper+se	
   	
  	
  	
  	
  Generic	
  Rules	
  
	
  
	
  
	
  
Typical	
  Credit	
  Card	
  Fraudster	
  
•  Use	
  stolen	
  cards	
  
•  Buy	
  Expensive	
  stuff	
  
•  In	
  Large	
  Quan++es	
  
•  Very	
  quickly	
  
•  At	
  odd	
  hours	
  
•  Ship	
  to	
  many	
  places	
  
•  Get	
  rejected	
  oXen	
  
CEP	
  Queries	
  
Moving	
  Averages	
  
from	
  Transac+onStream#window.+me(60	
  min)	
  	
  
select	
  itemNo,	
  avg(qty)	
  as	
  avg,	
  stdev(qty)	
  as	
  stdev	
  
group	
  by	
  itemNo	
  
update	
  AvgTbl	
  as	
  a	
  
on	
  itemNo	
  	
  ==	
  	
  a.itemNo;	
  
	
  
from	
  Transac+onStream	
  
[itemNo==	
  a.itemNo	
  and	
  qty	
  >	
  (a.avg	
  +	
  2*a.stdev)	
  in	
  AvgTbl	
  as	
  a]	
  
select	
  *	
  
insert	
  into	
  FraudStream;	
  
 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	
  
Transac=on	
  Velocity	
  
The	
  False	
  Posi=ve	
  Trap	
  
๏  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	
  
produc=ve	
  and	
  costly	
  
Rich	
  guy	
  
GiX	
  giver	
  
Impulse	
  Shopper	
  
Night	
  owl	
  
Many	
  girlfriends?	
  
	
  
How	
  to	
  avoid	
  False	
  Posi=ves	
  
•  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	
  
15	
  
Each	
  transac+on	
  is	
  classified	
  under	
  the	
  following	
  three	
  
quali+es	
  and	
  expressed	
  as	
  a	
  3	
  leper	
  token,	
  e.g.,	
  HNN	
  
	
  	
  
•  Amount	
  spent:	
  Low,	
  Normal	
  and	
  High	
  
•  Whether	
  the	
  transac#on	
  includes	
  high	
  price	
  
#cket	
  item:	
  Normal	
  and	
  High	
  
•  Time	
  elapsed	
  since	
  the	
  last	
  
transac#on:	
  Large,	
  Normal	
  and	
  Small	
  
Markov	
  Models:	
  Classifica=on	
  
16	
  
•  Create	
  a	
  State	
  Transi+on	
  Probability	
  Matrix	
  
	
  
	
  	
   LNL	
   LNH	
   LNS	
   LHL	
   HHL	
   HHS	
   HNS	
  
LNL	
  
0.976788	
   0.542152	
   0.20706	
   0.095459	
   0.007166	
   0.569172	
   0.335481	
  
LNH	
  
0.806876	
   0.609425	
   0.188628	
   0.651126	
   0.113801	
   0.630711	
   0.099825	
  
LNS	
  
0.07419	
   0.83973	
   0.951471	
   0.156532	
   0.12045	
   0.201713	
   0.970792	
  
LHL	
  
0.452885	
   0.634071	
   0.328956	
   0.786087	
   0.676753	
   0.063064	
   0.225353	
  
HHL	
  
0.386206	
   0.255719	
   0.451524	
   0.469597	
   0.810013	
   0.444638	
   0.612242	
  
HHS	
  
0.204606	
   0.832722	
   0.043194	
   0.459342	
   0.960486	
   0.796382	
   0.34544	
  
HNS	
  
0.757737	
   0.371359	
   0.326846	
   0.970243	
   0.771326	
   0.015835	
   0.574333	
  
Markov	
  Models:	
  Probability	
  Matrix	
  
17	
  
•  Compare	
  the	
  probabili+es	
  of	
  incoming	
  transac+on	
  
sequences	
  with	
  thresholds	
  and	
  flag	
  fraud	
  as	
  
appropriate	
  
•  Can	
  use	
  direct	
  probabili+es	
  or	
  more	
  complex	
  metrics	
  
–  Miss	
  Rate	
  Metric	
  
–  Miss	
  Probability	
  Metric	
  	
  
–  Entropy	
  Reduc+on	
  Metric	
  
•  Update	
  Markov	
  Probability	
  table	
  with	
  incoming	
  
transac+ons	
  
	
  
Markov	
  Models:	
  Probability	
  Comparison	
  
18	
  
Markov	
  Models	
  for	
  Fraud	
  Detec=on	
  
Learn	
  from	
  Data	
  	
  
•  Apply	
  Predic=ve	
  Analysis	
  on	
  Batch	
  Data	
  and	
  
provide	
  Classifiers	
  to	
  Streaming	
  Analy+cs	
  
Dig	
  Deeper	
  using	
  Big	
  Data	
  
•  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	
  
Visualize	
  
Visualize	
  
Contact	
  us	
  !	
  

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IMCSummit 2015 - Day 2 Developer Track - Catch Them in the Act - Fraud Detection in Real-time

  • 1. Catch  them  in  the  Act   Fraud  Detec+on  in  Real-­‐+me   Seshika  Fernando   Technical  Lead   WSO2     Catch  them   in  the  Act   Fraud  Detec+on   in  Real-­‐+me   Seshika  Fernando   Technical  Lead,  WSO2   seshika@wso2.com    
  • 2. $4 Trillion in Global Fraud Losses   That’s  5%  of  Global  GDP  
  • 3. Efficient  Fraud  Detec+on   Domain  Exper+se   Streaming   Analy+cs   Batch   Analy+cs   Predic+ve   Analy+cs  
  • 4. Complex  Event  Processing   No#fy  if  there  is  a  10%  increase  in  overall  trading  ac#vity  AND  the  average   price  of  commodi#es  has  fallen  2%  in  the  last  4  hours  
  • 5. Many  ways     •  Generic  Rules   •  Fraud  Scoring   •  Markov  Models   •  Machine  Learning  
  • 6. Domain  Exper+se          Generic  Rules        
  • 7. Typical  Credit  Card  Fraudster   •  Use  stolen  cards   •  Buy  Expensive  stuff   •  In  Large  Quan++es   •  Very  quickly   •  At  odd  hours   •  Ship  to  many  places   •  Get  rejected  oXen   CEP  Queries  
  • 8. Moving  Averages   from  Transac+onStream#window.+me(60  min)     select  itemNo,  avg(qty)  as  avg,  stdev(qty)  as  stdev   group  by  itemNo   update  AvgTbl  as  a   on  itemNo    ==    a.itemNo;     from  Transac+onStream   [itemNo==  a.itemNo  and  qty  >  (a.avg  +  2*a.stdev)  in  AvgTbl  as  a]   select  *   insert  into  FraudStream;  
  • 9.  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   Transac=on  Velocity  
  • 10. The  False  Posi=ve  Trap   ๏  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   produc=ve  and  costly   Rich  guy   GiX  giver   Impulse  Shopper   Night  owl   Many  girlfriends?    
  • 11. How  to  avoid  False  Posi=ves   •  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?    
  • 12. How  to  score   Score  =        0.001  *  itemPrice        +    0.1    *  itemQuan+ty          +    2.5    *  isFreeEmail          +    5    *  riskyCountry                    +    8    *  suspicousIPRange          +    5    *  suspicousUsername      +    3  *  highTransac+onVelocity      
  • 13. Are  we  safe  ?  
  • 14. Markov  Models   •  Model  randomly  changing  systems   •  Detect  rare  ac+vity  sequences  using   •  Classifica+on   •  Probability  Calcula+on   •  Metric  Calcula+on  
  • 15. 15   Each  transac+on  is  classified  under  the  following  three   quali+es  and  expressed  as  a  3  leper  token,  e.g.,  HNN       •  Amount  spent:  Low,  Normal  and  High   •  Whether  the  transac#on  includes  high  price   #cket  item:  Normal  and  High   •  Time  elapsed  since  the  last   transac#on:  Large,  Normal  and  Small   Markov  Models:  Classifica=on  
  • 16. 16   •  Create  a  State  Transi+on  Probability  Matrix         LNL   LNH   LNS   LHL   HHL   HHS   HNS   LNL   0.976788   0.542152   0.20706   0.095459   0.007166   0.569172   0.335481   LNH   0.806876   0.609425   0.188628   0.651126   0.113801   0.630711   0.099825   LNS   0.07419   0.83973   0.951471   0.156532   0.12045   0.201713   0.970792   LHL   0.452885   0.634071   0.328956   0.786087   0.676753   0.063064   0.225353   HHL   0.386206   0.255719   0.451524   0.469597   0.810013   0.444638   0.612242   HHS   0.204606   0.832722   0.043194   0.459342   0.960486   0.796382   0.34544   HNS   0.757737   0.371359   0.326846   0.970243   0.771326   0.015835   0.574333   Markov  Models:  Probability  Matrix  
  • 17. 17   •  Compare  the  probabili+es  of  incoming  transac+on   sequences  with  thresholds  and  flag  fraud  as   appropriate   •  Can  use  direct  probabili+es  or  more  complex  metrics   –  Miss  Rate  Metric   –  Miss  Probability  Metric     –  Entropy  Reduc+on  Metric   •  Update  Markov  Probability  table  with  incoming   transac+ons     Markov  Models:  Probability  Comparison  
  • 18. 18   Markov  Models  for  Fraud  Detec=on  
  • 19. Learn  from  Data     •  Apply  Predic=ve  Analysis  on  Batch  Data  and   provide  Classifiers  to  Streaming  Analy+cs  
  • 20. Dig  Deeper  using  Big  Data   •  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