Loan Eligibility Prediction Using Machine Learning
Kaivalya Gogula1, Nagaraju Chattu2
1Kaivalya Gogula, Masters of Science in Computer/Information Technology Administration and Management, St. Francis College, Brooklyn.
2Nagaraju Chattu, Masters of science in Business Analytics and Information Systems, University of South Florida, Tampa.
Manuscript received on 24 July 2024 | Revised Manuscript received on 20 August 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024 | PP: 12-15 | Volume-14 Issue-4, September 2024 | Retrieval Number: 100.1/ijsce.C814413030924 | DOI: 10.35940/ijsce.C8144.14040924
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Technology has made many improvements, and the banking industry is no exception. The submission of loan applications by individuals is so numerous every day, making it more difficult for banks to approve loans. To choose an applicant for loan approval, Banks must also consider their bank policies. Based on several factors, the bank must select the proposal with the best chance of being granted. It would be time-consuming and unsafe to individually verify each applicant’s information before recommending them for loan approval. Based on the prior performance of the person to whom the loan amount was previously allocated, we utilise a machine learning technique in this study to predict the person who is trustworthy for a loan. This will determine whether the applicant is eligible for the loan based on their previous loans and current outstanding loans, whether they are repaying the loan within the deadline, and other factors to shortlist the applicant as genuinely eligible for the loan or not.
Keywords: Machine Learning, Loan Approval, Random Forest, Dataset.
Scope of the Article: Computer Applications