1. Knowledge Discovery In Currency Risk Management

2. Goal • Increase Profit • Reduce Cost of Settlements • Increase Customer Satisfaction • Reduce Bank Risk • Reduce Capital Requirements

3. Domain • FX Trading System Relational Database • 6000 Customers • 400,000 FX Transactions • Demographic Information • Credit Information • FX Marketing Desk Customer Info Database • Marketer • Relationship Manager • Pricing Information

4. Foreign Exchange Primer • Spots and Forwards • Swaps • Window Options and Draw Downs • Multi-currency Accounts • Settlements • Customer Credit • Bank Risk

5. Methodology Action Rules are discovered to meet our Goals. For Example: • Geography( Canada ) AND CreditLine( NO -> YES) • => customerRating( Average -> Good ) • Confidence = 100% • Support = 52 Customers

6. Methodology • Data Extraction • SQL • Statistical Attributes • Data Nominalization • SQL • Range Mapping based on Domain Knowledge and Visualization • Data Reduction • SQL • 6,000 Customers to 2,500

7. Methodology • Rosetta • Reducts • Association Rules • Filtering

8. Methodology • Custom Application • Flexible versus Static Attributes • Association Rule combination • Filtering

9. Results • Spot-rating is Strongly correlated to the decision Attribute. • Spot-rating as flexible attribute ( 1058 Action Rules ) • Spot-rating as static attribute ( 99 Action Rules ) • Improving Spot-rating improves Customer-rating

10. Results • Some Customers would be more profitable by doing business with a CRM Interface Partner • 120 Supporting Customers • Static • Spot-rating = GOOD • Swap-volume = NONE • Flexible • primaryDealsrc( Direct -> (9 other partners) • Decision • BAD -> AVERAGE

11. Results • Some Customers would be more profitable by recovering settlement cost. • 118 Supporting Customers • Static • Spot-rating = GOOD • Swap-volume = NONE • Geography = US • Customer Type = Corporate • Flexible • Settlement-volume( Medium -> low or high ) • Decision • BAD -> AVERAGE

12. Results • Marketer EBF Could do Better • 68 Supporting Customers • Static • Spot-rating = GOOD • Swap-volume = NONE • Geography = US • Flexible • marketer( EBF -> {13 other} ) • Decision • BAD -> AVERAGE

13. Results • Marketer BKG Could do Better • 49 Supporting Customers • Static • Spot-rating = EXCELLENT • Swap-volume = NONE • Geography = US • Flexible • marketer( EBF -> {5 other} ) • Decision • AVERAGE -> GOOD

14. Next Steps • More holistic view of Profit & Loss of the products • More attributes--less derived attributes • Filter change to find rules with the most financial impact support, not number of customers supporting • Use methodology for continuous attributes to yield a more precise actions to take. E.g, increase spread from 3.2% to 3.4% to increase profitability by 5%

15. Questions? Thank You