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ca Securecenter 
Ten Commandments for Tackling Fraud: 
The Role of Big Data and Predictive Analytics 
Revathi Subramanian 
SCX11S #CAWorld 
SVP, Data Science 
CA Technologies
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© 2014 CA. ALL RIGHTS RESERVED. 
Abstract 
RevathiSubramanian 
CA TechnologiesSVP, Data Science 
Author of “Bank Fraud: Using Technology to Combat Losses” 
Accurate enterprise-wide data combined with data-driven fraud analytics can have a transformational effect on banking and related industries. Revathiwill provide tips and insights on using technologies like neural network predictive modeling, user behavior-based pattern recognition and statistical big data analytics to reduce the risk of fraudulent activities in the enterprise.
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© 2014 CA. ALL RIGHTS RESERVED. 
Current Challenges with Fraud Management 
FraudPrevention 
Operational Costs 
Customer 
Experience
4 
© 2014 CA. ALL RIGHTS RESERVED. 
It’s a balancing act. 
Achieving balance is the only way to increase customer acceptance. 
Failing to achieve balance results in lost business and unhappy customers. 
CUSTOMER 
EXPERIENCE 
FRAUD PREVENTION 
OPERATIONAL COSTS 
✔
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© 2014 CA. ALL RIGHTS RESERVED. 
1. Data: Garbage In; Garbage Out 
“On two occasions I have been asked, ‘pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ … I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.” Charles Babagge, ‘Passages from the Life of a Philosopher’
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© 2014 CA. ALL RIGHTS RESERVED. 
2. No documentation? No change! 
“When dealing with data, a rose by a different name is not the same as a rose.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
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© 2014 CA. ALL RIGHTS RESERVED. 
3. Key employees are not a substitute for good documentation. 
“Oh yeah! We made that change sometime in April and unfortunately we don’t have too many more details.”
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© 2014 CA. ALL RIGHTS RESERVED. 
4. Rules: More doesn’t mean better. 
“As it is with children or pets, having too many rules is generally counterproductive. Too many rules tend to confuse them. Interestingly, fraud management systems are no different.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
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© 2014 CA. ALL RIGHTS RESERVED. 
5. Score: Never rest on your laurels. 
“As fraud management systems get sophisticated, fraudsters also get sophisticated … Scoring processes have to keep on improving in order to tackle fraud effectively.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
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© 2014 CA. ALL RIGHTS RESERVED. 
Non-linear models are a requirement for any complex risk management problem. 
Modeling technology used needs to look beyond the “Brownian motion.” 
Needs to take into account the entire domain’s fraud patterns 
Needs to constantly improve –automated yearly refreshes with regional tweaks 
Advanced Analytical Scoring = Huge Value
11 
© 2014 CA. ALL RIGHTS RESERVED. 
6. Score + Rules = Winning Strategy 
“A sophisticated scoring system combined with a limited set of rules to take into account operational considerations is the winning combination … Having too many rules can water down the fraud management system, but even the best scores are not very useful unless the operational use of the scores is streamlined with some carefully crafted rules… .” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
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© 2014 CA. ALL RIGHTS RESERVED. 
Rules should be used to satisfy its business needs using a highly predictive score. 
Rules can also implement business policy. 
Business needs change over time and the rules need to change with that. 
One size does not fit all; There should be no need to go back to the tailor to get the best fit either.  
Scores tell you who might not be legitimate. 
Rules are what you decide to do with that knowledge. 
Any system that requires you to outsource rules is a problem. 
Maximum Precision with Maximum Flexibility
13 
© 2014 CA. ALL RIGHTS RESERVED. 
7. Fraud: It is everyone’s problem. 
“Every little bit of information we drop on the floor, every transaction that doesn’t get recorded, every rule that doesn’t get used right, every score that doesn’t get used optimally, every fraud analyst that doesn’t get trained well has an impact on the overall fraud management picture.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
14 
© 2014 CA. ALL RIGHTS RESERVED. 
Online fraud problem is huge. 
Digital footprint left behind by the customer is a goldmine of information that can be tracked and utilized with advanced analytics. 
Device/browser characteristics can identify issues very quickly and precisely using advanced analytics. 
Possible to track a device in real-time and nab the fraudster before major damage is done 
Digital World: Goldmine of Information
15 
© 2014 CA. ALL RIGHTS RESERVED. 
9. Fraud Control Systems: If they rest, they rust. 
“Fraud control systems have to evolve and change with the times, and the changes have to happen in an agile fashion.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
16 
© 2014 CA. ALL RIGHTS RESERVED. 
10. Continual Improvement: The cycle never ends. 
“Every time there is a leap forward in the digital world, there is an equal leap forward in what the fraudsters can do to increase the losses.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
17 
© 2014 CA. ALL RIGHTS RESERVED. 
Consider the important factors in a payment transaction: 
Each has unique history, distilled down to the critical information and combined with the transaction through a complex non-linear model to produce a score. 
Beyond the Simple: Pivots Aperson, thing or factor having a major or central role, function or effect 
Customer 
Card 
Device 
Merchant 
Customer 
Card 
Device 
Merchant 
Transaction 
789
18 
© 2014 CA. ALL RIGHTS RESERVED. 
For More Information 
To learn more about Security, please visit: 
http://bit.ly/10WHYDm 
Insert appropriate screenshot and textoverlayfrom following“More Info Graphics” slide here; ensure it links to correct page 
Security
19 
© 2014 CA. ALL RIGHTS RESERVED. 
For Informational Purposes Only 
© 2014CA. All rights reserved. All trademarks referenced herein belong to their respective companies. 
This presentation provided at CA World 2014 is intended for information purposes only and does not form any type of warranty. Some of the specific slides with customer references relate to customer's specific use and experience of CA products and solutionssoactual results may vary. 
Terms of this Presentation

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Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Analytics

  • 1. ca Securecenter Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Analytics Revathi Subramanian SCX11S #CAWorld SVP, Data Science CA Technologies
  • 2. 2 © 2014 CA. ALL RIGHTS RESERVED. Abstract RevathiSubramanian CA TechnologiesSVP, Data Science Author of “Bank Fraud: Using Technology to Combat Losses” Accurate enterprise-wide data combined with data-driven fraud analytics can have a transformational effect on banking and related industries. Revathiwill provide tips and insights on using technologies like neural network predictive modeling, user behavior-based pattern recognition and statistical big data analytics to reduce the risk of fraudulent activities in the enterprise.
  • 3. 3 © 2014 CA. ALL RIGHTS RESERVED. Current Challenges with Fraud Management FraudPrevention Operational Costs Customer Experience
  • 4. 4 © 2014 CA. ALL RIGHTS RESERVED. It’s a balancing act. Achieving balance is the only way to increase customer acceptance. Failing to achieve balance results in lost business and unhappy customers. CUSTOMER EXPERIENCE FRAUD PREVENTION OPERATIONAL COSTS ✔
  • 5. 5 © 2014 CA. ALL RIGHTS RESERVED. 1. Data: Garbage In; Garbage Out “On two occasions I have been asked, ‘pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ … I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.” Charles Babagge, ‘Passages from the Life of a Philosopher’
  • 6. 6 © 2014 CA. ALL RIGHTS RESERVED. 2. No documentation? No change! “When dealing with data, a rose by a different name is not the same as a rose.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
  • 7. 7 © 2014 CA. ALL RIGHTS RESERVED. 3. Key employees are not a substitute for good documentation. “Oh yeah! We made that change sometime in April and unfortunately we don’t have too many more details.”
  • 8. 8 © 2014 CA. ALL RIGHTS RESERVED. 4. Rules: More doesn’t mean better. “As it is with children or pets, having too many rules is generally counterproductive. Too many rules tend to confuse them. Interestingly, fraud management systems are no different.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
  • 9. 9 © 2014 CA. ALL RIGHTS RESERVED. 5. Score: Never rest on your laurels. “As fraud management systems get sophisticated, fraudsters also get sophisticated … Scoring processes have to keep on improving in order to tackle fraud effectively.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
  • 10. 10 © 2014 CA. ALL RIGHTS RESERVED. Non-linear models are a requirement for any complex risk management problem. Modeling technology used needs to look beyond the “Brownian motion.” Needs to take into account the entire domain’s fraud patterns Needs to constantly improve –automated yearly refreshes with regional tweaks Advanced Analytical Scoring = Huge Value
  • 11. 11 © 2014 CA. ALL RIGHTS RESERVED. 6. Score + Rules = Winning Strategy “A sophisticated scoring system combined with a limited set of rules to take into account operational considerations is the winning combination … Having too many rules can water down the fraud management system, but even the best scores are not very useful unless the operational use of the scores is streamlined with some carefully crafted rules… .” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
  • 12. 12 © 2014 CA. ALL RIGHTS RESERVED. Rules should be used to satisfy its business needs using a highly predictive score. Rules can also implement business policy. Business needs change over time and the rules need to change with that. One size does not fit all; There should be no need to go back to the tailor to get the best fit either.  Scores tell you who might not be legitimate. Rules are what you decide to do with that knowledge. Any system that requires you to outsource rules is a problem. Maximum Precision with Maximum Flexibility
  • 13. 13 © 2014 CA. ALL RIGHTS RESERVED. 7. Fraud: It is everyone’s problem. “Every little bit of information we drop on the floor, every transaction that doesn’t get recorded, every rule that doesn’t get used right, every score that doesn’t get used optimally, every fraud analyst that doesn’t get trained well has an impact on the overall fraud management picture.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
  • 14. 14 © 2014 CA. ALL RIGHTS RESERVED. Online fraud problem is huge. Digital footprint left behind by the customer is a goldmine of information that can be tracked and utilized with advanced analytics. Device/browser characteristics can identify issues very quickly and precisely using advanced analytics. Possible to track a device in real-time and nab the fraudster before major damage is done Digital World: Goldmine of Information
  • 15. 15 © 2014 CA. ALL RIGHTS RESERVED. 9. Fraud Control Systems: If they rest, they rust. “Fraud control systems have to evolve and change with the times, and the changes have to happen in an agile fashion.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
  • 16. 16 © 2014 CA. ALL RIGHTS RESERVED. 10. Continual Improvement: The cycle never ends. “Every time there is a leap forward in the digital world, there is an equal leap forward in what the fraudsters can do to increase the losses.” RevathiSubramanian, Bank Fraud: Using Technology to Combat Losses
  • 17. 17 © 2014 CA. ALL RIGHTS RESERVED. Consider the important factors in a payment transaction: Each has unique history, distilled down to the critical information and combined with the transaction through a complex non-linear model to produce a score. Beyond the Simple: Pivots Aperson, thing or factor having a major or central role, function or effect Customer Card Device Merchant Customer Card Device Merchant Transaction 789
  • 18. 18 © 2014 CA. ALL RIGHTS RESERVED. For More Information To learn more about Security, please visit: http://bit.ly/10WHYDm Insert appropriate screenshot and textoverlayfrom following“More Info Graphics” slide here; ensure it links to correct page Security
  • 19. 19 © 2014 CA. ALL RIGHTS RESERVED. For Informational Purposes Only © 2014CA. All rights reserved. All trademarks referenced herein belong to their respective companies. This presentation provided at CA World 2014 is intended for information purposes only and does not form any type of warranty. Some of the specific slides with customer references relate to customer's specific use and experience of CA products and solutionssoactual results may vary. Terms of this Presentation