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Artificial Intelligence for Banking Fraud Prevention

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Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.

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Artificial Intelligence for Banking Fraud Prevention

  1. 1. Artificial Intelligence at NetGuardians: From skepticism to large scale adoption towards fraud prevention ©NetGuardians / 2018
  2. 2. © 2018 NetGuardians SA. All right reserved2 Jérôme Kehrli • Engineering and Computer Science background • CTO • NetGuardians for 3.5 years. • 18 years in the Software Engineering business, most of it in financial institutions twitter.com/JeromeKehrli linkedin.com/in/jeromekehrli
  3. 3. © 2018 NetGuardians SA. All right reserved3 NetGuardians - TOP European FinTech Funded in 2008 50 customers 60 employees • Behavioral analysis based on risk models combining human actions relative to channels, technical layers and transactions. • Stay on top of new anti-fraud patterns using Artificial Intelligence LayersChannels Transactions
  4. 4. Artificial Intelligence for Banking Fraud Prevention A bit of history, from NetGuardians’ perspective.
  5. 5. © 2018 NetGuardians SA. All right reserved5 Before 2000, banking fraud detection relies mostly on … 2008 2015 2016 2017 2018 • Manual Controls … • Internal control, • Internal Audit, • External Audits, etc. • … but also • the Operational Information System, • some BI reports.
  6. 6. © 2018 NetGuardians SA. All right reserved6 First steps : rule-based approach. • In the late 2000’s, cost of fraud and complexity of attacks increases. • Banking Institutions deploy analytics systems for fraud prevention • Rule engines (often AML) • Nobody seriously considers Artificial Intelligence and Machine Learning • NetGuardians was a rule engine 2008 2015 2016 2017 2018 IF payment destination country is risky (e.g. Russia) AND payment amount is greater than 10’000 CHF THEN flag transaction for review
  7. 7. © 2018 NetGuardians SA. All right reserved7 Example : The Bangladesh Bank Heist https://www.bankinfosecurity.com/bangladeshi-bank-hackers-steal-100m-a-8958
  8. 8. © 2018 NetGuardians SA. All right reserved8 Example : The Bangladesh Bank Heist http://www.dhakatribune.com /business/banks/2017/03/28/ muhith-stolen-heist-money- must-recovered/
  9. 9. © 2018 NetGuardians SA. All right reserved9 Example : The Bangladesh Bank Heist
  10. 10. © 2018 NetGuardians SA. All right reserved10 Another Example : The Retefe saga… “This threat actor has already been around for more than four years... Their goal remains the same: committing e-banking fraud in Switzerland and Austria. In August 2017, Retefe still redirects between 10 and 90 e-banking sessions every day. “ https://www.govcert.admin.ch/blog/33/the-retefe-saga
  11. 11. © 2018 NetGuardians SA. All right reserved11 Facts and projections Fraud costs the world $3 trillion per year in2017 Certified Fraud Examiners, Report to the Nations, 2014 $6 trillion Projected cyber crime cost by 2021 Cyber Security Ventures, 2016 It takes 18 months on average to detect an internal fraud. Most remains undetected. Certified Fraud Examiners, Report to the Nations, 2014 $6 trillion $3 trillion The big one The Bangladesh bank heist is one of the biggest bank heist ever and the biggest cybercrime in history $81 million
  12. 12. Rule-based systems are beaten ! Every bank customer / user is different Hundreds of thousands of rules would be required to reflect everyone’s situation Financial Impacts Reputation Damage
  13. 13. © 2018 NetGuardians SA. All right reserved13 Artificial Intelligence comes in help The machine can learn about habits of individuals and detect suspicious transactions • Analysis of transactions on several years  Learn about habits and behaviors of customers and employees  Build dynamic profiles  Keep profiles up-to-date in real-time • Compare transactions with customer/user profile • Compute a risk core and take a decision 2008 2015 2016 2017 2018
  14. 14. Lacking a global view of activities at the bank scale Some transactions are always unusual on a per customer basis Financial Gains Reputation Operational Efficiency Drastic reduction of fraud cases passing through Number of cases to be investigated reduced to 1/3 Number of re-validation asked to customers reduced to 1/4 Average time required to investigate a case reduced by 80%
  15. 15. © 2018 NetGuardians SA. All right reserved15 The Machine can do better Group individuals based on their similarities and compare a transaction to the group • Analysis of transactions on several years • Broad Vision – Big Picture  Discover and learn peer groups: the customers or employees with same habits and same behavior  Build peer group profiles dynamically • Compare transactions to the customer and peer group profiles 2008 2015 2016 2017 2018
  16. 16. Additional reduction of cases to be investigated (false positives) Groups and their profiles form an invaluable information source Additional Operational Efficiency Additional Financial Gains Analyzing non-transactional activity requires different analysis techniques Analysis of weak signals related to behavioural changes
  17. 17. © 2018 NetGuardians SA. All right reserved17 Even further … 2008 2015 2016 2017 2018 For instance Internet Banking applications: Learn about non-transactional behavior paths and qualify individual interactions based on path-to-action • Analyze all interactions between individuals and the bank IS • Probabilistic learning of path-to-action • Compare every single individual interaction with model • Customer-based / group-based (as usual) • Applications : Ebanking, EAM, API banking, PSD2, etc. Genuine User Login Account Balance Payment Input Payment Validation Pending Orders Logout Worm(virus) Login Payment Input Payment Validation Logout
  18. 18. IA vs. IA Reputation Operational Efficiency Detect Fraud before it happens! Enhanced scoring models Protect customer identity and privacy
  19. 19. Conclusion
  20. 20. © 2018 NetGuardians SA. All right reserved20 Artificial Intelligence helps secure banks and their customers Drastic reduction of fraud cases passing through Number of cases to be investigated reduced to 1/3 Number of revalidation asked to customers reduced to 1/4 Average time required to investigate a case reduced by 80% Financial Gains Reputation Operational Efficiency  AI sublimates anomaly detection • All interactions between individuals and the banking IS as well as all financial transactions are monitored • Real-time anomaly detection  AI performs large scale monitoring of human behavior to secure banks and their customers • Science fiction vs reality …
  21. 21. Computer Analytics Big Data Real-time Analytics Versatile Data Capture Machine Learning Lambda Architecture User Experience Cloud Computing AI Pillars at NetGuardians
  22. 22. © 2018 NetGuardians SA. All right reserved23 KMA Centre , 7th floor, Mara Road Upper Hill, Nairobi, Kenya T +254 204 93 11 96 NetGuardians Africa 143 Cecil Street #09-01 GB Building 069542 Singapore T +65 6224 0987 NetGuardians Asia Koszykowa 61, 00-667 Warsaw, Poland NetGuardians Eastern Europe Y-Parc, Av. des Sciences 13 1400 Yverdon-les-Bains Switzerland T +41 24 425 97 60 F +41 24 425 97 65 NetGuardians Headquarters Rhein-Main Gebiet Germany T +49 172 3799003 NetGuardians Germany @netguardians Linkedin.com/company/netguardians Facebook.com/NetGuardians www.netguardians.ch info@netguardians.ch +41 24 425 97 60 Contact us THANK YOU!

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