Title: A genetic programming-based credit risk assessment model

Authors: Ashutosh Vashishtha; Shivankit Andotra; Amit Kant Pandit; Shubham Mahajan

Addresses: School of Business, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India ' School of Business, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India ' School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India ' School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India

Abstract: The acute necessity for evolving an effective and accurate credit default prediction model was felt post Global Financial Crisis of 2008. Financial institutions significantly revised and reformulated their risk management practices and gradually shifted towards machine learning-based credit risk management approach. Numerous machine learning-based models like logistic regression, artificial neural networks, decision trees, etc., are being employed by financial institutions for predicting the probability of default by the borrowers. In this paper, we introduce a genetic program (GP)-based model for predicting the probability of default and compare this model with other existing models in the domain of credit default and risk assessment. We used two different evaluation metrics for performance analysis: accuracy and negative log predictive density (NLPD) loss. Our results indicate that the proposed GP-based model has higher accuracy of prediction of credit default as compared to other risk assessment models.

Keywords: credit risk management; machine learning; artificial neural network; decision tree; credit risk GP-based model.

DOI: 10.1504/IJBG.2025.146480

International Journal of Business and Globalisation, 2025 Vol.40 No.3, pp.201 - 209

Received: 05 Aug 2021
Received in revised form: 16 Jan 2022
Accepted: 18 Mar 2022
Published online: 02 Jun 2025 *

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