Work2023

Intelligent Loan Eligibility and Approval System based on Random Forest Algorithm using Machine Learning

C. Prasanth · Ranjeet Kumar · A. Rangesh · N. Sasmitha · B Dhiyanesh

22 citations0/0 files storedInternational Conference on Innovative Data Communication Technologies and Application (ICIDCA)

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Financial Distress and Bankruptcy Prediction Microfinance and Financial Inclusion Data Mining and Machine Learning Applications

Abstract

According to the banking system, loans are a major source of income for everyone. So, loans provide the biggest profit for the banking sector. Regardless of how many people apply for bank loans nowadays, finding a genuine candidate who will repay the loan is difficult. People rely on massive amounts of data in the banking industry on a daily basis as a result of digitization. This study has suggested an interactive system that prioritizes consumer wait times based on predictive analysis of future occurrences. Every day, a new loan application system emerges. When a customer asks for a loan, the bank will decide whether or not to approve it based on the customer's eligibility and requirements. Here, the machine learning algorithms are utilized to forecast the past consumer data and decide the ultimate clearance. The Random Forest (RF) Algorithm assists in accurately predicting the customer's loan repayment and recording it for future prediction. The proposed algorithm improves accuracy while decreasing the time complexity.

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2020An Approach for Prediction of Loan Approval using Machine Learning Algorithm 2022LOAN APPROVAL PREDICTION BASED ON MACHINE LEARNING APPROACH 2020Bank Loan Prediction System using Machine Learning 2022Customer Loan Eligibility Prediction using Machine Learning Algorithms in Banking Sector 2022Machine Learning Models for Predicting Bank Loan Eligibility 2022Machine Learning Based Model for Prediction of Loan Approval 2020Predict Loan Approval in Banking System Machine Learning Approach for Cooperative Banks Loan Approval

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