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What to Learn from Near Misses: An Inductive Learning Approach to Credit Risk Assessment
Author(s) -
Tessmer Antoinette Canart
Publication year - 1997
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1997.tb01304.x
Subject(s) - dimension (graph theory) , decision tree , computer science , process (computing) , credit risk , stability (learning theory) , machine learning , credit score , artificial intelligence , credit analysis , tree (set theory) , actuarial science , credit reference , business , mathematics , mathematical analysis , pure mathematics , operating system
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.