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
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
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For Corporates: +91-9618483483
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