This document summarizes a project to reduce fraudulent card transactions for a US national bank. An ensemble technique using logistic regression and K-nearest neighbors was developed to classify transactions as fraudulent or legitimate in real time. The project was estimated to reduce fraudulent losses by $16-18 million while costing $4.2 million to develop. Testing on 1 year of transaction data accurately classified transactions and reduced fraudulent cases by 80-90%, saving the bank $16 million.

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.

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% 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

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