What to Learn from Near Misses: An Inductive Learning Approach to Credit Risk Assessment

Abstract

ABSTRACT This paper presents a new dimension of inductive learning for credit risk analysis based on the specific impact of Type I and Type II credit errors on the accuracy of the learning process. A Dynamic Updating Process is proposed to refine the credit granting decision over time and therefore improve the accuracy of the learning process. The new dimension is tested on credit files of small Belgian businesses. Results indicate an improvement of the learning process in terms of predictive accuracy, stability, and conceptual validity of the final decision tree.

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