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

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.

Country : Sri Lanka

1 Senarathna B.T.N2 Weerarathna K.C.M3 Wickramarachchi D.S4 Jayarathne S.M.P.N5 Buddhima Attanyake

  1. Department of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Department of Computer Systems Engineering Specializes in Information Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 10, October 2023 pp. 532-542

doi.org/10.47001/IRJIET/2023.710070

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