Under circumstances where data quality may vary, knowledge about the potential
performance of alternate predictive models can enable a decision maker to design an
information system whose value is optimized in two ways. The decision maker can select
a model which is least sensitive to predictive degradation in the range of observed data
quality variation. And, once the "right" model has been selected, the decision maker can
select the appropriate level of data quality in view of the costs of acquiring it. This paper
examines a real-world example from the field of finance -- prepayments in mortgage-backed
securities (MBS) portfolio management -- to illustrate a methodology that enables such
evaluations to be made for two modeling alternative: regression analysis and neural network
analysis. The methodology indicates that with "perfect data," the neural network approach
outperforms regression in terms of predictive accuracy and utility in a prepayment risk
management forecasting system (RMFS). Further, the performance of the neural network
model is more robust under conditions of data quality degradation.Information Systems Working Papers Serie