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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big JOINT Statistics / Financial Mathematics SEMINAR}
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\centerline{12:00 p.m., Friday, January 31, 2003}
\centerline{Sequoia Hall Room 200}
\centerline{(Note: Special Fair, Isaac Pizza party following the talk downstairs, 1:00-3:00)}

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\centerline{\sl Gerald Fahner}
\centerline{\sl Prediction Technology Unit, Fair, Issac}
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\centerline{\bf Improving Credit Card Marketing with Learning Strategies}
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Statistical methods can be of significant value to improve
and optimize the behavior of complex systems, such as chemical plants,
or consumers' usage of credit cards.

The speaker will  give an overview over current testing practices used
by many lenders/marketers for the crucial task of improving response
rate  and profitability from credit offers, and point out some problems
and inefficiencies  associated with these approaches. The talk gives a
description of  how more elaborate statistical methods of experimental
design and regression analysis can reap additional benefits of faster
learning and increased confidence in performance estimates and
optimization results. It will be discussed  how some unique business-
and operational constraints in this industry can be tackled by searching
for constrained optimal experimental designs, and illustrate this
approach with case study results.


Please address any questions or comments to Bogdan Popescu
(bogdan@stat.stanford.edu).

******NOTE: There will be a pizza party, sponsored by Fair, Isaac \& Co
following the talk in the lounge of Sequoia Hall. Everyone is invited to
attend.

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