Metaheuristic-Driven Feature Selection for Machine Learning-Based Credit Scoring
Received: 18 October 2025 | Revised: 16 November 2025 | Accepted: 3 December 2025 | Online: 11 December 2025
Corresponding author: Chinh Xuan Pham
Abstract
The recurrence of financial and debt crises in recent years has underscored the critical importance of effective credit risk management in financial research. Within this context, credit scoring is a critical tool for financial institutions to evaluate loan applications and has received extensive attention in both statistical and machine learning research. This study proposes a novel credit scoring framework that integrates metaheuristic-driven feature selection with machine learning classifiers. Three metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Tabu Search (TS), are employed to identify the most relevant subset of features, whereas a wide range of machine learning models is trained to determine the most effective combination for credit scoring. The framework is evaluated on three benchmark datasets, namely Australian, German, and Japanese datasets, from the UCI Machine Learning Repository. Experimental results show that metaheuristic-based feature selection consistently improves model performance compared to the baseline without feature selection and conventional methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Stepwise Selection, demonstrating its effectiveness and robustness in credit scoring tasks.
Keywords:
credit scoring, feature selection, metaheuristic search, machine learningDownloads
References
J. C. Hull, Risk Management and Financial Institutions, 6th ed. Hoboken, NJ, USA: John Wiley & Sons, 2023.
R. Sengupta and G. Bhardwaj, "Credit Scoring and Loan Default," International Review of Finance, vol. 15, no. 2, pp. 139–167, June 2015.
F. Louzada, A. Ara, and G. B. Fernandes, "Classification methods applied to credit scoring: Systematic review and overall comparison," Surveys in Operations Research and Management Science, vol. 21, no. 2, pp. 117–134, Dec. 2016.
X. Dastile, T. Celik, and M. Potsane, "Statistical and machine learning models in credit scoring: A systematic literature survey," Applied Soft Computing, vol. 91, June 2020, Art. no. 106263.
Z. Song and H. Mo, "Transformative Impacts of Big Data Technologies on the Credit Reporting Industry: Drivers, Challenges, and Future Trajectories," Journal of Frontier in Economic and Management Research, vol. 1, no. 1, pp. 292–314, Sept. 2025.
W. A. Addy, A. O. Ajayi-Nifise, B. G. Bello, S. T. Tula, O. Odeyemi, and T. Falaiye, "AI in credit scoring: A comprehensive review of models and predictive analytics," Global Journal of Engineering and Technology Advances, vol. 18, no. 2, pp. 118–129, Feb. 2024.
A. A. H. Raji, A. H. F. Alabdoon, and A. Almagtome, "AI in Credit Scoring and Risk Assessment: Enhancing Lending Practices and Financial Inclusion," in 2024 International Conference on Knowledge Engineering and Communication Systems, Chikkaballapur, India, 2024, pp. 1–7.
A. Ampountolas, T. Nyarko Nde, P. Date, and C. Constantinescu, "A Machine Learning Approach for Micro-Credit Scoring," Risks, vol. 9, no. 3, Mar. 2021, Art. no. 50.
E. Dumitrescu, S. Hué, C. Hurlin, and S. Tokpavi, "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, vol. 297, no. 3, pp. 1178–1192, Mar. 2022.
O. A. Bello, "Machine Learning Algorithms for Credit Risk Assessment: An Economic and Financial Analysis," International Journal of Management Technology, vol. 10, no. 1, pp. 109–133, Dec. 2023.
M. Ala’raj, M. F. Abbod, M. Majdalawieh, and L. Jum’a, "A deep learning model for behavioural credit scoring in banks," Neural Computing and Applications, vol. 34, no. 8, pp. 5839–5866, Apr. 2022.
F. Shen, X. Zhao, G. Kou, and F. E. Alsaadi, "A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique," Applied Soft Computing, vol. 98, Jan. 2021, Art. no. 106852.
C. X. Pham, H. N. Trinh, and L. Q. Tran, "A Robust Approach to Credit Scoring with Deep Learning and Embedded Methods," Engineering, Technology & Applied Science Research, vol. 15, no. 6, pp. 29284–29291, Dec. 2025.
V. Moscato, A. Picariello, and G. Sperlí, "A benchmark of machine learning approaches for credit score prediction," Expert Systems with Applications, vol. 165, Mar. 2021, Art. no. 113986.
B. Baesens, T. Van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen, "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, vol. 54, no. 6, pp. 627–635, June 2003.
S. Lessmann, B. Baesens, H.-V. Seow, and L. C. Thomas, "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, vol. 247, no. 1, pp. 124–136, Nov. 2015.
Y. Hayashi, "Emerging Trends in Deep Learning for Credit Scoring: A Review," Electronics, vol. 11, no. 19, Oct. 2022, Art. no. 3181.
H. Demma Wube, S. Zekarias Esubalew, F. Fayiso Weldesellasie, and T. Girma Debelee, "Deep Learning and Machine Learning Techniques for Credit Scoring: A Review," in Second Pan-African Conference on Artificial Intelligence, Addis Ababa, Ethiopia, 2023, pp. 30–61.
N. Bussmann, P. Giudici, D. Marinelli, and J. Papenbrock, "Explainable Machine Learning in Credit Risk Management," Computational Economics, vol. 57, no. 1, pp. 203–216, Jan. 2021.
B. H. Misheva, J. Osterrieder, A. Hirsa, O. Kulkarni, and S. F. Lin, "Explainable AI in Credit Risk Management." arXiv, Mar. 01, 2021.
G. Manikandan, S. Abirami, O. P. Jena, A. R. Tripathy, A. A. Elngar, and Z. Polkowski, "Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art," in Computational Intelligence and Healthcare Informatics, Hoboken, NJ, USA: John Wiley & Sons, Ltd, 2021, pp. 43–63.
Y. Wu and Z. Huang, "Feature selection considering feature relevance, redundancy and interactivity for neighbourhood decision systems," Neurocomputing, vol. 596, Sept. 2024, Art. no. 128092.
J. Laborda and S. Ryoo, "Feature Selection in a Credit Scoring Model," Mathematics, vol. 9, no. 7, Apr. 2021, Art. no. 746.
Y. Zhou, M. S. Uddin, T. Habib, G. Chi, and K. Yuan, "Feature selection in credit risk modeling: an international evidence," Economic Research-Ekonomska Istraživanja, vol. 34, no. 1, pp. 3064–3091, Jan. 2021.
G. Chandrashekar and F. Sahin, "A survey on feature selection methods," Computers & Electrical Engineering, vol. 40, no. 1, pp. 16–28, Jan. 2014.
A. Biernacki, "Evaluating Filter, Wrapper, and Embedded Feature Selection Approaches for Encrypted Video Traffic Classification," Electronics, vol. 14, no. 18, Sept. 2025, Art. no. 3587.
A. Siham, S. Sara, and A. Abdellah, "Feature selection based on machine learning for credit scoring : An evaluation of filter and embedded methods," in 2021 International Conference on INnovations in Intelligent SysTems and Applications, Kocaeli, Turkey, 2021, pp. 1–6.
Á. Paz et al., "Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review," Biomimetics, vol. 10, no. 5, May 2025, Art. no. 326.
T. Dokeroglu, A. Deniz, and H. E. Kiziloz, "A comprehensive survey on recent metaheuristics for feature selection," Neurocomputing, vol. 494, pp. 269–296, July 2022.
V. G. Helder, T. P. Filomena, L. Ferreira, and G. Kirch, "Application of the VNS heuristic for feature selection in credit scoring problems," Machine Learning with Applications, vol. 9, Sept. 2022, Art. no. 100349.
S. Oreski and G. Oreski, "Genetic algorithm-based heuristic for feature selection in credit risk assessment," Expert Systems with Applications, vol. 41, no. 4, Part 2, pp. 2052–2064, Mar. 2014.
R. Estran, A. Souchaud, and D. Abitbol, "Using a genetic algorithm to optimize an expert credit rating model," Expert Systems with Applications, vol. 203, Oct. 2022, Art. no. 117506.
D. Martens, T. Van Gestel, M. De Backer, R. Haesen, J. Vanthienen, and B. Baesens, "Credit rating prediction using Ant Colony Optimization," Journal of the Operational Research Society, vol. 61, no. 4, pp. 561–573, Apr. 2010.
F. Atban, M. Y. Küçükkara, and C. Bayılmış, "Enhancing variational quantum classifier performance with meta-heuristic feature selection for credit card fraud detection," The European Physical Journal Special Topics, vol. 234, no. 15, pp. 3705–3718, Oct. 2025.
B. K. Verma and A. K. Yadav, "Advancing Software Vulnerability Scoring: A Statistical Approach with Machine Learning Techniques and GridSearchCV Parameter Tuning," SN Computer Science, vol. 5, no. 5, May 2024, Art. no. 595.
J. Inga and E. Sacoto-Cabrera, "Credit Default Risk Analysis Using Machine Learning Algorithms with Hyperparameter Optimization," in VIII International Conference on Science, Technology and Innovation for Society, Guayaquil, Ecuador, 2022, pp. 81–95.
B. Xue, M. Zhang, and W. N. Browne, "Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach," IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1656–1671, Dec. 2013.
R. Meiri and J. Zahavi, "Using simulated annealing to optimize the feature selection problem in marketing applications," European Journal of Operational Research, vol. 171, no. 3, pp. 842–858, June 2006.
H. Zhang and G. Sun, "Feature selection using tabu search method," Pattern Recognition, vol. 35, no. 3, pp. 701–711, Mar. 2002.
R. Quinlan, "Statlog (Australian Credit Approval)." UCI Machine Learning Repository, 1987.
H. Hofmann, "Statlog (German Credit Data)." UCI Machine Learning Repository, 1994.
C. Sano, "Japanese Credit Screening." UCI Machine Learning Repository, 1992.
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Copyright (c) 2025 Chinh Xuan Pham, Huynh Ngoc Trinh, Long Quoc Tran
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