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Banking Domain

   Prashanth Vajjhala
   Deepika Kancherla
       Sushma Ponna
       Shruthi Reddy
   Siddhartha Paturu
Domain & Challenges


Portfolio
                 Probability of Default
Allocation



                                           Fraud Detection
Top Performing
                 Highest Value Customers
Agents



Churn
Business Problem
A US national bank which has a revenue of $10 billion, is
losing about 2% of it’s revenue, i.e $20 million, due to
fraudulent card transactions.
Objective
 Minimize fraudulent cases
Consultation
 Reduce the fraudulent cases by about 80-90%.
 Losses curtailed: $16 - $18 million
 Price of Information (Including Product Cost): $4.2
 million
Data:
 Approximately 1 year data
 500,000 records
 2% fraud and 98% legitimate
Attributes:
 Location, Customer ID, Date, Time, Transaction
  Amount, Account ID, Reference ID, Transaction
  Code, Membership Period, Credit Card
  Limit, Fraudulent Cases (Yes/No)
Architecture:

                 System 2
• Neural                     • K – Nearest
  Networks    • Logistic       Neighbours
                Regression

   System 1                     System 3
Method:

Ensembler Technique



                         Logistic Regression   K-NN




10 Input Nodes
1 Hidden Layer
8 Hidden Nodes
2 Output Nodes
Logistic (x) Squashing      Shared Memory
Function
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
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.
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
Results:
                  $16 million saving!!
  25
            20
  20

  15
                                         Initial Losses
  10
                                         After
                              4
   5                                     implementation

  0
       Initial Losses        After
                        implementation
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-engineering

This presentation may contain references to findings of various reports available in the public domain. INSOFE makes no representation as to their accuracy or that the
organization subscribes to those findings.

The best place for students to learn Applied Engineering                                                                http://www.insofe.edu.in

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Fraud detection

  • 1. Banking Domain Prashanth Vajjhala Deepika Kancherla Sushma Ponna Shruthi Reddy Siddhartha Paturu
  • 2. Domain & Challenges Portfolio Probability of Default Allocation Fraud Detection Top Performing Highest Value Customers Agents Churn
  • 3. Business Problem A US national bank which has a revenue of $10 billion, is losing about 2% of it’s revenue, i.e $20 million, due to fraudulent card transactions.
  • 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% legitimate Attributes:  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-NN 10 Input Nodes 1 Hidden Layer 8 Hidden Nodes 2 Output Nodes Logistic (x) Squashing Shared Memory Function
  • 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-engineering This presentation may contain references to findings of various reports available in the public domain. INSOFE makes no representation as to their accuracy or that the organization subscribes to those findings. The best place for students to learn Applied Engineering http://www.insofe.edu.in