A Review on Loan Approval Prediction Based On Machine Learning Techniques

Year : 2024 | Volume :15 | Issue : 02 | Page : –
By

Gaurav Raj Baser

Sadhna K. Mishra

  1. M_tech Scholar Dept. of computer science Engineering, LNCT. Bhopal Madhya Pradesh India
  2. Professor Dept. of Computer science Engineering, LNCT. Bhopal Madhya Pradesh India

Abstract

The banking industry has also benefited greatly from technological advancements. An increasing number of individuals are submitting loan applications on a daily basis. When deciding which loan applicants to approve, the bank must take certain rules into account. The bank needs to choose the best one for approval based on certain characteristics. The process of carefully verifying every person and recommending them for loan approval is laborious and fraught with danger. Currently, machine learning is extremely popular. In today’s technologically advanced society, machine Algorithms for learning govern and manage almost all applications. After forecast whether a loan application will be approved or not, several ML models, often used for classification algorithms, are developed and evaluated in different tasks. This review article investigates the key combination of machine learning approaches with loan approval prediction in the banking sector. The study uses supervised and unsupervised machine learning methods to create a prediction model based on a dataset of previous customers of banks. The paper also thoroughly investigates the components of loan eligibility, the sorts of risks encountered in the banking industry, and the numerous classification methods used. This detailed analysis adds to a better understanding of the complex interactions between machine learning and loan processes within the banking sector.

Keywords: Loan Approval, Banking Sector Machine Learning, Classification Methods.

[This article belongs to Journal of Computer Technology & Applications(jocta)]

How to cite this article: Gaurav Raj Baser, Sadhna K. Mishra. A Review on Loan Approval Prediction Based On Machine Learning Techniques. Journal of Computer Technology & Applications. 2024; 15(02):-.
How to cite this URL: Gaurav Raj Baser, Sadhna K. Mishra. A Review on Loan Approval Prediction Based On Machine Learning Techniques. Journal of Computer Technology & Applications. 2024; 15(02):-. Available from: https://journals.stmjournals.com/jocta/article=2024/view=148406

References

  1. Nureni AA, Adekola OE. Loan approval prediction based on machine learning approach. FUDMA JOURNAL OF SCIENCES. 2022 Jun 24;6(3):41-50.
  2. Mamun MA, Farjana A, Mamun M. Predicting Bank Loan Eligibility Using Machine Learning Models and Comparison Analysis. InProceedings of the 7th North American International Conference on Industrial Engineering and Operations Management, Orlando, FL, USA 2022 Jun (pp. 12-14).
  3. Aphale AS, Shinde SR. Predict loan approval in banking system machine learning approach for cooperative banks loan approval. International Journal of Engineering Trends and Applications (IJETA). 2020 Aug;9(8).
  4. V. S. Kiran, B. T. Reddy, D. U. Kumar, K. S. A. Varma, and T. S. Kiran, “Loan Eligibility Prediction Using Machine Learning,” Int. J. Res. Appl. Sci. Eng. Technol., 2023, doi: 10.22214/ijraset.2023.55132. https://www.ijraset.com/research-paper/loan-eligibility-prediction-using-machine-learning
  5. Hegde SK, Hegde R, Marthanda AV, Logu K. Performance analysis of machine learning algorithm for the credit risk analysis in the banking sector. In2023 7th International Conference on Computing Methodologies and Communication (ICCMC) 2023 Feb 23 (pp. 57-63). IEEE.
  6. Akça MF, Sevli O. Predicting acceptance of the bank loan offers by using support vector machines. International Advanced Researches and Engineering Journal. 2022 Aug 8;6(2):142-7.
  7. Shruti Mishra, Shailki Sharma and Shreyansh Singh. Loan approval prediction. Int Journal of Circuit, Computing and Networking. 2022;3(2)44-48.
  8. Samreen A, Zaidi FB. Design and development of credit scoring model for the commercial banks of Pakistan: Forecasting creditworthiness of individual borrowers. International Journal of Business and Social Science. 2012 Sep 1;3(17).
  9. Sheikh MA, Goel AK, Kumar T. An approach for prediction of loan approval using machine learning algorithm. In2020 international conference on electronics and sustainable communication systems (ICESC) 2020 Jul 2 (pp. 490-494). IEEE.
  10. Boddepalli, “Loan Eligibility Criteria using Machine Learning,” Int. J. Res. Publ. Rev., vol. 3, 2022. https://ijrpr.com/uploads/V3ISSUE11/IJRPR7738.pdf
  11. Miss Hemangini N Jadav. RISK MANAGEMENT IN INDIAN BANKS: SOME ISSUES AND CHALLENGES. Research Guru: Online Journal of Multidisciplinary Subjects (Peer Reviewed). 2017;11(3):302-309.
  12. Kargi HS. Credit risk and the performance of Nigerian banks. Ahmadu Bello University, Zaria. 2011 Jul;13(9):44-6.
  13. Boateng KW, Dean YN. Credit risk management and profitability in select savings and loans companies in Ghana. International Journal of Advanced Research. 2020;1.
  14. Aphale AS, Shinde SR. Predict loan approval in banking system machine learning approach for cooperative banks loan approval. International Journal of Engineering Trends and Applications (IJETA). 2020 Aug;9(8).
  15. Viswanatha V, Ramachandra AC, Vishwas KN, Adithya G. Prediction of Loan Approval in Banks using Machine Learning Approach. International Journal of Engineering and Management Research. 2023 Aug 2;13(4):7-19.
  16. Tejaswini J, Kavya TM, Ramya RD, Triveni PS, Maddumala VR. Accurate loan approval prediction based on machine learning approach. Journal of Engineering Science. 2020;11(4):523-32.
  17. Orji UE, Ugwuishiwu CH, Nguemaleu JC, Ugwuanyi PN. Machine learning models for predicting bank loan eligibility. In2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON) 2022 Apr 5 (pp. 1-5). IEEE.
  18. Meenaakumari M, Jayasuriya P, Dhanraj N, Sharma S, Manoharan G, Tiwari M. Loan Eligibility Prediction using Machine Learning based on Personal Information. In2022 5th International Conference on Contemporary Computing and Informatics (IC3I) 2022 Dec 14 (pp. 1383-1387). IEEE.
  19. Park MS, Son H, Hyun C, Hwang HJ. Explainability of machine learning models for bankruptcy prediction. Ieee Access. 2021 Sep 3;9:124887-99.
  20. Xu J, Lu Z, Xie Y. Loan default prediction of Chinese P2P market: a machine learning methodology. Scientific Reports. 2021 Sep 21;11(1):18759.
  21. Meshref, Hossam. (2020). Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms. Circuits Systems and Signal Processing. 14. 914-922. 10.46300/9106.2020.14.117.

Regular Issue Subscription Review Article
Volume 15
Issue 02
Received February 28, 2024
Accepted April 9, 2024
Published May 30, 2024