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Knowledge Discovery

** Explore how to increase profit, reduce costs, and manage bank risk in FX trading with discovered action rules based on customer data analysis. Utilize Data Extraction, SQL, and statistical methods to improve decision-making. **

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Knowledge Discovery

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  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

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