Decision Sciences Journal
Volume 28, Number 1
Winter 1997
What to Learn from Near Misses: An Inductive Learning Approach to Credit Risk Assessment
Antoinette Canart Tessmer
Department of Finance, University of Illinois at Urbana-Champaign, 340 Commerce West, 1206 South Sixth Street, Champaign, IL 61820, atessmer@uiuc.edu
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.
Subject Areas: Decision Support Systems, Decision Theory, and Finance.
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