Method and apparatus system for modeling consumer capacity for future incremental debt in credit scoring

Abstract

Predicting impact of future actions on subsequent creditworthiness involves developing a prediction model that predicts a statistical interaction of performance expectation with likely post-scoring behavior. Including sensitivity to new, post-scoring date credit behaviors in the analytic solution greatly improves snapshot score predictions. The modeling approach involves multiple snapshots: predictive and performance snapshots, plus an intermediate snapshot shortly after the predictive snapshot to quantify interim consumer behavior post-scoring date. Predictive interaction variables are calculated on the predictive data using simulated consumer profiles before and after assuming a sizeable simulated balance to infer the consumer's tolerance for incremental future debt. Using an adjustor approach in predicting capacity allows isolation of the confounding effect of risk from the capacity determination. A resulting capacity index can be used to rank order originations and line increases according to capacity in consumer, bankcard, automobile and mortgage lending.