International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Loan Eligibility Prediction Based on Credit Score and Past History

Senarathna B.T.NDepartment of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaWeerarathna K.C.MDepartment of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaWickramarachchi D.SDepartment of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaJayarathne S.M.P.NDepartment of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaBuddhima AttanyakeDepartment of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 7 No 10 (2023): Volume 7, Issue 10, October 2023 | Pages: 532-542

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 05-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.710070

pdf iconFull Text PDF
Abstract

A sophisticated system for predicting loan eligibility that takes credit scores and history into account is urgently needed due to the banking sector's rapid expansion. This study paper integrates the contributions of four separate components put forth by several researchers to present a comprehensive and new strategy. Using cutting-edge machine learning algorithms like Random Forest, Gradient Boosting, and Linear Regression, the first component predicts the best bank rate possibilities. The second component, which focuses on determining applicants' loan eligibility, presents a revolutionary technique that uses their credit histories and considers important factors including their gender, marital status, level of education, income, and credit history itself. The third element suggests a sophisticated Loan Eligibility Prediction System that includes several crucial sub-objectives, including creditworthiness assessment, income and employment verification, collateral analysis, risk profiling, fraud detection, and regulatory compliance, to ensure thorough risk analysis. To forecast the applicant’s risk level efficiently and effectively, this component applies the power of logistic regression. The results are laid out in a simple tabular format for simple understanding. To help with mortgage estimation and analysis, the fourth component includes a sophisticated mortgage calculator tool that uses Decision Tree Regression. Using this tool, users may calculate and evaluate mortgage values based on important property characteristics like the number of bathrooms, beds, the size of the house, and the location. This research study suggests a novel and complete loan eligibility prediction system that combines these four elements and makes use of historical data, cutting-edge machine learning methods, and credit ratings. This approach facilitates rapid and successful lending processes while avoiding risks and maximizing outcomes, empowering both borrowers and lenders to make knowledgeable decisions.

Keywords

Loan eligibility prediction system, Credit scores and history, Machine learning algorithms, Risk analysis, Mortgage estimation and analysis


Citation of this Article

Senarathna B.T.N, Weerarathna K.C.M, Wickramarachchi D.S, Jayarathne S.M.P.N, Buddhima Attanyake, “Loan Eligibility Prediction Based on Credit Score and Past History” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 10, pp 532-542, October 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.710070

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Sheikh, M. A., Goel, A. K., & Kumar, T. (2020, July). An approach for prediction of loan approval using machine learning algorithm. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 490-494). IEEE.
  2. Kumar, A., Sharma, S., & Mahdavi, M. (2021). Machine learning (ML) technologies for digital credit scoring in rural finance: A literature review. Risks, 9(11), 192.
  3. Malekipirbazari, M., & Aksakalli, V. (2015). Risk assessment in social lending via random forests. Expert Systems with Applications, 42(10), 4621-4631.
  4. isutsa, G. T. (2021). Loan Default Prediction Using Machine Learning: a Case of Mobile Based Lending (Doctoral dissertation, University of Nairobi).
  5. Kumar, C. N., Keerthana, D., Kavitha, M., & Kalyani, M. (2022, June). Customer Loan Eligibility Prediction using Machine Learning Algorithms in Banking Sector. In 2022 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 1007-1012). IEEE.
  6. Kadam, A. S., Nikam, S. R., Aher, A. A., Shelke, G. V., & Chandgude, A. S. (2021). Prediction for loan approval using machine learning algorithm. International Research Journal of Engineering and Technology (IRJET), 8(04).
  7. Mohankumar, M., Amuthakkani, S., & Jeyamala, G. (2016). Comparative analysis of decision tree algorithms for the prediction of eligibility of a man for availing bank loan. Age, 19, 60.
  8. Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12(1), 469477.
  9. Chen, S., Härdle, W. K., & Jeong, K. (2010). Forecasting volatility with support vector machine‐based GARCH model. Journal of  Forecasting, 29(4), 406-433.
  10. Odegua, R. (2020). Predicting bank loan default with extreme gradient boosting. arXiv preprint arXiv:2002.02011.
  11. Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14(1), 1-24.
  12. Adekoya, A. F., Nti, I. K., & Weyori, B. A. (2022). Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi. FinTech, 1(1), 25-43.
  13. Ma, X., Sha, J., Wang, D., Yu, Y., Yang, Q., & Niu, X. (2018). Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electronic Commerce Research and Applications, 31, 24-39.
  14. Stein, G., Chen, B., Wu, A. S., & Hua, K. A. (2005, March). Decision tree classifier for network intrusion detection with GA-based feature selection. In Proceedings of the 43rd annual Southeast regional conference Volume 2 (pp. 136-141).
  15. Wu, J. M. T., Li, Z., Herencsar, N., Vo, B., & Lin, J. C. W. (2021). A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Systems, 1-20.
  16. Sripriya, S. V. S., Varrey, S. D. S., & Venkateshkumar, M. (2022, October). Predictive Model to Compute Eligibility Test for Loans. In 2022 IEEE Industrial Electronics and Applications Conference (IEACon) (pp. 185-190). IEEE.
  17. Madaan, M., Kumar, A., Keshri, C., Jain, R., & Nagrath, P. (2021). Loan default prediction using decision trees and random forest: A comparative study. In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012042). IOP Publishing.
  18. Sripriya, S. V. S., Varrey, S. D. S., & Venkateshkumar, M. (2022, October). Predictive Model to Compute Eligibility Test for Loans. In 2022 IEEE Industrial Electronics and Applications Conference (IEACon) (pp. 185-190). IEEE.
  19. Amin, R. K., & Sibaroni, Y. (2015, May). Implementation of decision tree using C4. 5 algorithm in decision making of loan application by debtor (Case study: Bank pasar of Yogyakarta Special Region). In 2015 3rd International Conference on Information and Communication Technology (ICoICT) (pp. 75-80). IEEE.
  20. Min, S. H., Lee, J., & Han, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert systems with applications, 31(3), 652-660.
  21. Liu, W., Fan, H., & Xia, M. (2022). Credit scoring based on tree enhanced gradient boosting decision trees. Expert Systems with Applications, 189, 116034.
  22. Ala’raj, M., Abbod, M. F., & Majdalawieh, M. (2021). Modelling customers credit card behaviour using bidirectional LSTM neural networks. Journal of Big Data, 8(1), 1-27.
  23. Ghaddar, B., & Naoum-Sawaya, J. (2018). High dimensional data classification and feature selection using support vector machines. European Journal of Operational Research, 265(3), 993-1004.
  24. Tumuluru, P., Burra, L. R., Loukya, M., Bhavana, S., CSaiBaba, H. M. H., & Sunanda, N. (2022, February). Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms. In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 349-353). IEEE.
  25. Al-Qerem, A., Al-Naymat, G., & Alhasan, M. (2019, December). Loan default prediction model improvement through comprehensive preprocessing and features selection. In 2019 International Arab Conference on Information Technology (ACIT) (pp. 235-240). IEEE.
  26. Huang, X., Liu, X., & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317324.
  27. Li, S. T., Shiue, W., & Huang, M. H. (2006). The evaluation of consumer loans using support vector machines. Expert Systems with Applications, 30(4), 772-782.
  28. Gupta, K., Chakrabarti, B., Ansari, A. A., Rautaray, S. S., & Pandey, M. (2021, April). Loanification-Loan Approval Classification using Machine Learning Algorithms. In Proceedings of the International Conference on Innovative Computing & Communication (ICICC).
  29. Geller, A., & Hainaut, D. Long Short-Term Memory neural network for econometric forecasting: A comparison between a statistical method and a neural network in the case of Value at Risk.
  30. Mahbobi, M., Kimiagari, S., & Vasudevan, M. (2021). Credit risk classification: an integrated predictive accuracy algorithm using artificial and deep neural networks. Annals of Operations Research, 1-29.
  31. Abedin, M. Z., Guotai, C., Hajek, P., & Zhang, T. (2022). Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk. Complex & Intelligent Systems, 121.
  32. Shrestha, S., & Paudel, L. (2019). Classification of Loan Applications of Garima Bikas Bank Ltd Using Decision Tree Classification Method. Journal of Advanced College of Engineering and Management, 5, 147-152.
  33. Zhu, L., Qiu, D., Ergu, D., Ying, C., & Liu, K. (2019). A study on predicting loan default based on the random forest algorithm. Procedia Computer Science, 162, 503-513.
  34. Sadeeq, M. A., & Abdulazeez, A. M. (2020, December). Neural networks architectures design, and applications: A review. In 2020 International Conference on Advanced Science and Engineering (ICOASE) (pp. 199-204). IEEE.
  35. Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42(2), 741-750.
  36. Nica, I., Alexandru, D. B., Crăciunescu, S. L. P., & Ionescu, Ș. (2021). Automated valuation modelling: analysing mortgage behavioural life profile models using machine learning techniques. Sustainability, 13(9), 5162.
  37. Mahbobi, M., Kimiagari, S., & Vasudevan, M. (2021). Credit risk classification: an integrated predictive accuracy algorithm using artificial and deep neural networks. Annals of Operations Research, 1-29.
  38. Rasmussen, C. E. (1997). Evaluation of Gaussian processes and other methods for non-linear regression (Doctoral dissertation, University of Toronto Toronto, Canada).
  39. Song, Y., Wang, Y., Ye, X., Wang, D., Yin, Y., & Wang, Y. (2020). Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending. Information Sciences, 525, 182-204.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.