Loan Eligibility Prediction Using Machine Learning
Article Sidebar
Main Article Content
Abstract
Technology has made many improvements, and the banking industry is no exception. Submission of loan applications by people are so many everyday, making it more difficult for bank to approve loan. To choose an applicant for loan approval, Banks must consider other bank policies also. Based on a few factors, the bank must choose the proposal that has the best probability of getting granted. It would be time-consuming and unsafe to individually check each applicant before recommending them for loan approval. Based on the prior performance of the person to whom the loan amount was previously accredited, we utilize a machine learning technique in this study to forecast the person who is trustworthy for a loan. This will check the whether the applicant is eligible for the loan or not based upon the any previous loan or running loans whether the applicant is paying back the loan within the deadline or not and it will check many other factors to shortlist the applicant is genuinely eligible for loan or not
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Kumar Arun, Garg Ishan, Kaur Sanmeet, May- Jun. 2016. Loan Approval Prediction based on Machine Learning Approach, IOSR Journal of Computer Engineering (IOSR-JCE).
Dr. K. Kavitha, International Journal of Advanced Research in Computer Science and Software Engineering.
K. Hanumantha Rao, G. Srinivas, A. Damodhar, M. Vikas Krishna: Implementation of Anomaly Detection Technique Using Machine Learning Algorithms: International Journal of Computer Science and Telecommunications (Volume2, Issue3, June 2011).
Clustering Loan Applicants based on Risk Percentage using K-Means Clustering Techniques,
Short-term prediction of Mortgage default using ensembled machine learning models, Jesse C.Sealand on july 20, 2018.
https://www.ibm.com/in-en/topics/random- forest#:~:text=Random%20forest%20is%20a%20c ommonly,both%20classification%20and%20regres sion%20problems.
https://www.researchgate.net/publication/35744912 6_THE_LOAN_PREDICTION_USING_MACHIN E_LEARNING
https://ieeexplore.ieee.org/document/9336801
Nixon, J. S., & Desta, A. W. (2020). Data Mining Application in Predicting Bank Loan Defaulters. In International Journal of Innovative Technology and Exploring Engineering (Vol. 4, Issue 9, pp. 2733–2744). https://doi.org/10.35940/ijitee.d2037.029420
Prasad, K. G. S., Chidvilas, P. V. S., & Vasanthamisan, V. K. (2019). Customer Loan Approval Classification by Supervised Learning Model. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 9898–9901). https://doi.org/10.35940/ijrte.d9275.118419
Gupta, R., Gowalker, N., Patil, D. S., & Joshi, Dr. S. D. (2019). Predicting Risk in Sentiment Analysis using Machine Learning. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 455–460). https://doi.org/10.35940/ijeat.a9540.109119
Mukherjee, P., Palan, P., & Bonde, M. V. (2021). Using Machine Learning and Artificial Intelligence Principles to Implement a Wealth Management System. In International Journal of Soft Computing and Engineering (Vol. 10, Issue 5, pp. 26–31). https://doi.org/10.35940/ijsce.f3500.0510521
Dubey, S. K., Sinha, Dr. S., & Jain, Dr. A. (2023). Heart Disease Prediction Classification using Machine Learning. In International Journal of Inventive Engineering and Sciences (Vol. 10, Issue 11, pp. 1–6). https://doi.org/10.35940/ijies.b4321.11101123
Sharma, P., & Site, S. (2022). A Comprehensive Study on Different Machine Learning Techniques to Predict Heart Disease. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 2, Issue 3, pp. 1–7). https://doi.org/10.54105/ijainn.c1046.042322
Baig, M. A. (2021). An Efficient Cluster Based Routing Protocol (ECCRP) Technique Based on Weighted Clustering Algorithm for Different Topologies in Manets using Network Coding. In Indian Journal of Data Communication and Networking (Vol. 1, Issue 2, pp. 31–34). https://doi.org/10.54105/ijdcn.b5011.041221
Patravali, S. D., & Algur, Dr. S. P. (2023). COVID-19 Sentiment Analysis using K-Means and DBSCAN. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 12, pp. 12–17). https://doi.org/10.35940/ijese.l2558.11111223