machine learningfor bank loan eligibility prediction focus on home loan and eduction loan
D SHANMUGA PRUDVI,M SANTHOSH,Moulishwar S
Published in International Journal of Advanced Research in Computer Networking,Wireless and Mobile Communications
ISSN: 2320-7248 Impact Factor:1.8 Volume:5 Issue:3 Year: 21 April,2025 Pages:256-261
Abstract
Presently a day’s individual approach or select bank credits to fulfill their needs, which are exceptionally common. [1]. This hone has been expanding day by day. The loan is one of the most important schemes of bank [2]. Banks typically offer loans to customers in accordance with their needs. However, unfortunately, some clients are unable to pay their debts on time or delay doing so because of their financial situation.[3] Several people take advantage and misuse the facilities given by the bank. In order to solve this problem Banks must employ certain approaches to assist in anticipating the loan repayment status.[4] Banking system always need accurate modelling system for large number of issues. The ability to predict credit defaulters is one of the most challenging tasks for any bank [5] However,by predicting the loan defaulters, the banks will undoubtedly be able to cut their loss by decreasing their non-profit assets, allowing the recovery of sanctioned loans to proceed without incurring any losses and acting as a contributing factor to the bank statement.
Kewords
MACHINE LEARNING, LOAN ELIGIBILITY PREDICTION, HOME AND EDUCTION LOAN.
Reference
[1] C. Liu, Y. Ming, Y. Xiao, W. Zheng and C. - H. Hsu, "Finding the Next Interesting Loan for Investors on a Peer-to-Peer Lending Platform," in IEEE Access, vol. 9, pp. 111293-111304, 2021, doi: 10.1109/ACCESS.2021.3103510. [2] M. S. Park, H. Son, C. Hyun and H. J. Hwang, "Explainability of Machine Learning Models for Bankruptcy Prediction," in IEEE Access, vol. 9, pp. 124887-124899, 2021, doi: 10.1109/ACCESS.2021.3110270. [3] Z. Li, D. Fu and H. Li, "Dynamic Forecasting for Systemic Risk in China’s Commercial Banking Industry Based on Sequence Decomposition and Reconstruction," in IEEE Access, vol. 11, pp. 132068-132077, 2023, doi: 10.1109/ACCESS.2023.3335609 [4] T. Nguyen et al., "Multi-Swarm Optimization for Extracting Multiple-Choice Tests FromQuestion Banks," in IEEE Access, vol. 9, pp. 32131-32148, 2021, doi: 10.1109/ACCESS.2021.3057515. [5] I. Met, A. Erkoç and S. E. Seker, "Performance, Efficiency, and Target Setting for Bank Branches: Time SeriesWith Automated Machine Learning," in IEEE Access, vol. 11, pp. 1000-1010, 2023, doi: 10.1109/ACCESS.2022.3233529. [6] S. K. Depren and M. T. Kartal, Prediction on the volume of non-performing loans in Turkey using multivariate adaptive regression splines approach, International Journal of Finance & Economics, vol. 26, no. 4, pp. 6395–6405, 2021. [7] T. Bozdoğan, ‘‘Analysis of financial performance of foreign banks having branches in Turkey by TOPSIS and ELECTRE methods,’’ Alanya Akademik Bakış, vol. 5, no. 2, pp. 1049–1067, Mar. 2021 [8] K. G. No, S. Niroomand, H. Didehkhani, and A. Mahmoodirad, ‘‘Modified interval EDAS approach for the multi-criteria ranking problem in banking sector of Iran,’’ J. Ambient Intell. Humanized Comput., vol. 12, no. 7, pp. 8129–8148, Jul. 2021. [9] J. Reig-Mullor and J. M. Brotons-Martinez, ‘‘The evaluation performance for commercial banks by intuitionistic fuzzy numbers: The case of Spain,’’ Soft Comput., vol. 25, no. 14, pp. 9061–9075, Jul. 2021. [10] G. Szepannek and K. Lübke, ‘‘Facing the challenges of developing fair risk scoring models,’’ Frontiers Artif. Intell., vol. 4, pp. 1–9, Oct. 2021.
