knowledge discovery
DESCRIPTION
Knowledge Discovery. In Currency Risk Management. Goal. Increase Profit Reduce Cost of Settlements Increase Customer Satisfaction Reduce Bank Risk Reduce Capital Requirements. Domain. FX Trading System Relational Database 6000 Customers 400,000 FX Transactions Demographic Information - PowerPoint PPT PresentationTRANSCRIPT
Knowledge DiscoveryIn Currency Risk Management
Goal
• Increase Profit
• Reduce Cost of Settlements
• Increase Customer Satisfaction
• Reduce Bank Risk
• Reduce Capital Requirements
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
Foreign Exchange Primer
• Spots and Forwards
• Swaps
• Window Options and Draw Downs
• Multi-currency Accounts
• Settlements
• Customer Credit
• Bank Risk
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
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
Trad ingS ystem
C ustom er In foS ystem
C onso lida teedD ata
N om ina lizedD ata
Methodology
N om ina lizedD ata
R osetta
S upportingA ssocia tion
R ules
• Rosetta
– Reducts
– Association Rules
– Filtering
Methodology
• Custom Application
– Flexible versus Static Attributes
– Association Rule combination
– Filtering
S upportingA ssocia tion
R ules
S upportingA ction R u les
Action.java
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
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
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
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
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
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%
Questions?Thank You