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