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Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Fraud detection
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Fraud detection

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Student Presentation on Real World Business Cases : BFSI Domain.

Student Presentation on Real World Business Cases : BFSI Domain.

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  • 1. Banking Domain Prashanth Vajjhala Deepika Kancherla Sushma Ponna Shruthi Reddy Siddhartha Paturu
  • 2. Domain & ChallengesPortfolio Probability of DefaultAllocation Fraud DetectionTop Performing Highest Value CustomersAgentsChurn
  • 3. Business ProblemA US national bank which has a revenue of $10 billion, islosing about 2% of it’s revenue, i.e $20 million, due tofraudulent card transactions.
  • 4. Objective Minimize fraudulent cases
  • 5. Consultation Reduce the fraudulent cases by about 80-90%. Losses curtailed: $16 - $18 million Price of Information (Including Product Cost): $4.2 million
  • 6. Data: Approximately 1 year data 500,000 records 2% fraud and 98% legitimateAttributes: Location, Customer ID, Date, Time, Transaction Amount, Account ID, Reference ID, Transaction Code, Membership Period, Credit Card Limit, Fraudulent Cases (Yes/No)
  • 7. Architecture: System 2• Neural • K – Nearest Networks • Logistic Neighbours Regression System 1 System 3
  • 8. Method:Ensembler Technique Logistic Regression K-NN10 Input Nodes1 Hidden Layer8 Hidden Nodes2 Output NodesLogistic (x) Squashing Shared MemoryFunction
  • 9. Cost Estimates: 3 machines, 1 shared memory 6 machines per state 1 server Machine Cost, Server cost & Shared memory cost: $100,000 – one time investment Back up machines: 50 ~ $15,000 Server Maintenance Cost: $20,000 per year Total Cost incurred: $115,000 one time + $20,000 per year maintenance
  • 10. Product 1 – 3 Scale rating Aim to classify any new transaction as fraudulent or not on the basis of the rating. Any transaction with an average rating of 2.7 or more is flagged “RED” indicating with more than 90% evidence. Alert sent to the Bank and Customer immediately. Evaluation is done real time.
  • 11. Product Pricing 2 months to analyse the data. 4 months to build models and test and improve. Project Requires – 12 Analysts, 2 Managers Cost To Company for employees: - $504,000 + $120,000 = $624,000 Additional Expenses approximately $300,000. Price of building Product: Approx $924,000
  • 12. Results: $16 million saving!! 25 20 20 15 Initial Losses 10 After 4 5 implementation 0 Initial Losses After implementation
  • 13. International School of Engineering 2-56/2/19, Khanamet, Madhapur, Hyderabad - 500 081 For Individuals: +91-9177585755 or 040-65743991 For Corporates: +91-9618483483 Web: http://www.insofe.edu.in Facebook: http://www.facebook.com/insofe Twitter: https://twitter.com/INSOFEedu YouTube: http://www.youtube.com/InsofeVideos SlideShare: http://www.slideshare.net/INSOFE LinkedIn: http://www.linkedin.com/company/international- school-of-engineeringThis presentation may contain references to findings of various reports available in the public domain. INSOFE makes no representation as to their accuracy or that theorganization subscribes to those findings.The best place for students to learn Applied Engineering http://www.insofe.edu.in

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