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Biomimetics
Volume 10
Issue 5
10.3390/biomimetics10050326
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Systematic Review

Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review

by
Álex Paz
1,2,
Broderick Crawford
3,*,
Eric Monfroy
2,
José Barrera-García
3,
Álvaro Peña Fritz
1,
Ricardo Soto
3,
Felipe Cisternas-Caneo
3 and
Andrés Yáñez
1,2
1
Escuela de Ingeniería en Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
2
Laboratoire d’Étude et de Recherche en Informatique d’Angers (LERIA), Université d’ Angers, 49000 Angers, France
3
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2374631, Chile
*
Author to whom correspondence should be addressed.
Biomimetics 2025, 10(5), 326; https://doi.org/10.3390/biomimetics10050326 (registering DOI)
Submission received: 3 April 2025 / Revised: 3 May 2025 / Accepted: 13 May 2025 / Published: 17 May 2025
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
Versions Notes

Abstract

Credit risk assessment plays a critical role in financial risk management, focusing on predicting borrower default to minimize losses and ensure compliance. This study systematically reviews 23 empirical articles published between 2019 and 2023, highlighting the integration of machine learning and optimization techniques, particularly bio-inspired metaheuristics, for feature selection in individual credit risk assessment. These nature-inspired algorithms, derived from biological and ecological processes, align with bio-inspired principles by mimicking natural intelligence to solve complex problems in high-dimensional feature spaces. Unlike prior reviews that adopt broader scopes combining corporate, sovereign, and individual contexts, this work focuses exclusively on methodological strategies for individual credit risk. It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. To strengthen transparency and comparability, this review also synthesizes classification performance metrics—such as accuracy, AUC, F1-score, and recall—reported across benchmark datasets. Although no unified experimental comparison was conducted due to heterogeneity in study protocols, this structured summary reveals consistent trends in algorithm effectiveness and evaluation practices. The review concludes with practical recommendations and outlines future research directions to improve fairness, scalability, and real-time application in credit risk modeling.
Keywords: individual credit risk assessment; machine learning; metaheuristic; feature selection; bio-inspired algorithms; benchmark datasets; evaluation metrics individual credit risk assessment; machine learning; metaheuristic; feature selection; bio-inspired algorithms; benchmark datasets; evaluation metrics

Share and Cite

MDPI and ACS Style

Paz, Á.; Crawford, B.; Monfroy, E.; Barrera-García, J.; Fritz, Á.P.; Soto, R.; Cisternas-Caneo, F.; Yáñez, A. Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review. Biomimetics 2025, 10, 326. https://doi.org/10.3390/biomimetics10050326

AMA Style

Paz Á, Crawford B, Monfroy E, Barrera-García J, Fritz ÁP, Soto R, Cisternas-Caneo F, Yáñez A. Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review. Biomimetics. 2025; 10(5):326. https://doi.org/10.3390/biomimetics10050326

Chicago/Turabian Style

Paz, Álex, Broderick Crawford, Eric Monfroy, José Barrera-García, Álvaro Peña Fritz, Ricardo Soto, Felipe Cisternas-Caneo, and Andrés Yáñez. 2025. "Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review" Biomimetics 10, no. 5: 326. https://doi.org/10.3390/biomimetics10050326

APA Style

Paz, Á., Crawford, B., Monfroy, E., Barrera-García, J., Fritz, Á. P., Soto, R., Cisternas-Caneo, F., & Yáñez, A. (2025). Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review. Biomimetics, 10(5), 326. https://doi.org/10.3390/biomimetics10050326

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