Comparative Analysis of Machine Learning Algorithms for Loan Approval Prediction

References

[1] O. M. Ayad, A. Hegazy, and A. Dahroug, "A Proposed Model for Loan Approval Prediction Using Explainable Artificial Intelligence," in 2023 IEEE Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS 2023), 2023.

[2] S. Aravinth et al., "AI-Powered Credit Scoring Models: Enhancing Accuracy and Reducing Bias in Loan Approvals," in 4th IEEE World Conference on Applied Intelligence and Computing (AIC), 2025.

[3] A. Andhale, O. Bansode, R. Chavan, and J. Bagade, "Bank Loan Prediction System By Using Machine Learning," in 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA), 2024.

[4] S. Aravinth et al., "Comparative study on Loan Approval Prediction Utilizing Machine Learning Approaches," in 7th International Conference on Contemporary Computing and Informatics (IC3I), 2024.

[5] C. N. Kumar, D. Keerthana, M. Kavitha, and M. Kalyani, "Customer Loan Eligibility Prediction using Machine Learning Algorithms in Banking Sector," in 7th International Conference on Communication and Electronics Systems (ICCES), 2022.

[6] A. Julian and R. Booshanram, "Data-Driven Decisions: A Machine Learning Perspective on Loan Authorization," in International Conference on Emerging Smart Computing and Informatics (ESCI), 2022.

[7] N. Shrama et al., "Enhancing Financial Decision-Making: Loan Approval Prediction Using Machine Learning," in 2nd International Conference on Integration of Computational Intelligent System (ICICIS), 2025.

[8] M. Gahlot, Y. Barve, R. Panchal, G. Sakarkar, and V. Dhawale, "Loan Defaulter Prediction using Machine Learning: A Comparative Analysis with Explainability Insights," in 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), 2025.

[9] U. E. Orji and C. H. Ugwuishiwu, "Machine Learning Models for Predicting Bank Loan Eligibility," in IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), 2022.

[10] R. Ratan, S. S. Yadav, R. Malhotra, and A. Kumar, "Predicting Bank Loan Approval with Artificial Intelligence," in 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 2024.

[11] M. K. Mounagurusamy et al., "Prediction of Loan Approval in Banks Using Machine Learning Algorithm," in International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG), 2024.

[12] A. V. A. Reddy, C. Dinesh, G. K. Kousik, and J. C, "Towards Intelligent Lending: Predicting Loan Approvals with Machine Learning," in 5th International Conference on Intelligent Technologies (CONIT), 2025.

[13] N. Deborah R et al., "An Efficient Loan Approval Status Prediction Using Machine Learning," in International Conference on Advanced Computing Technologies and Applications (ICACTA), 2023.

[14] S. Sharmila, P. V. S. Sandhya, P. S. Kousar, P. Anuradha, and S. Deekshitha, "Bank Loan Approval using Machine Learning," in International Conference on Integrated Circuits and Communication Systems (ICICACS), 2024.

[15] E. Kadam, A. Gupta, I. Dubey, S. Jagtap, and G. Tawde, "Loan Approval Prediction System using Logistic Regression and CIBIL Score," in 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), 2023.

@article{1717946,
      author = {Maria Monish E, Dr. M N Nachappa},
      title = {Comparative Analysis of Machine Learning Algorithms for Loan Approval Prediction},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2680-2687},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717946.pdf},
      abstract = {Manual loan evaluation within the contemporary banking sector is an increasingly tedious and time-consuming process, frequently prone to human error, inconsistencies, and high operational costs. While automated systems have been introduced to improve efficiency, a significant research gap exists because many current machine learning models function as "black boxes". This lack of interpretability prevents bankers from justifying loan denials to applicants or complying with evolving "right to explanation" transparency regulations. The purpose of this study is to propose a hybrid framework that balances high predictive accuracy with fairness and explainability in credit decision-making. The methodology integrates advanced ensemble learning techniques, specifically Random Forest and XGBoost, with Explainable AI (XAI) tools such as SHAP (Lundberg & Lee) and LIME. To ensure the model remains robust against real-world data challenges, the framework utilizes SMOTE (Synthetic Minority Over-sampling Technique) for addressing dataset imbalances and the Artificial Bee Colony (ABC) algorithm for nature-inspired hyperparameter optimization. By processing diverse borrower attributes—including applicant income, credit history, and CIBIL scores—the implementation is expected to achieve a predictive accuracy exceeding 95%. Furthermore, the integration of fairness-aware learning mechanisms is projected to reduce algorithmic bias by approximately 22.4%. This provides the local and global explanations necessary for a transparent, "human-in-the-loop" financial system, shifting the primary decision-making role from a rigid algorithm back to the informed banker. Ultimately, this framework supports sustainable banking goals by minimizing non-performing assets while promoting inclusive and responsible lending practices.},
      keywords = {Loan Approval Prediction, Machine Learning, Classification Algorithms, Credit Risk Assessment, Comparative Analysis, Supervised Learning},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717946}
  }