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Purpose

The purpose of this paper is to provide a comprehensive decision support approach in credit risk assessment.

Design/methodology/approach

A comprehensive decision support approach is proposed for credit scoring and prediction. The predictive performance of the new approach has been investigated by using data including number and text.

Findings

The results demonstrate that the proposed approach achieves better and more stable classification accuracy than the single classifiers in most cases. Meanwhile, the prediction accuracy of individual classifiers is also improved by the proposed approach.

Originality/value

This study provides a comprehensive model for credit risk scoring and provides valuable information to the existing literature on credit scoring by using artificial intelligence.

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