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