Credit Risk Evaluation for Loan Approval

Author(s)	Prof. RUPALI LAXMAN KAMTHE, Prof. AKSHATA VIJAY LEMBHE, Prof. DEEPALI SUNIL AKOLKAR, Prof. SEEMA NITIN DOKRIMARE
Country	India
Abstract	The loan approval process plays a vital role in the financial sector, requiring careful assessment of an applicant’s financial background to determine their eligibility. This project introduces a machine learning-based model designed to predict loan approval outcomes using applicant data. The model is developed using a dataset that includes key variables such as income, credit history, loan amount, and other relevant financial indicators. To build a reliable prediction system, we apply algorithms like decision trees and gradient boosting, along with cross-validation methods to improve accuracy and generalization. The findings highlight the model’s ability to accurately distinguish between approved and rejected applications. Incorporating this predictive tool into financial workflows can help institutions make informed decisions, improve operational efficiency, and manage risk more effectively. Overall, the proposed system presents a data-driven solution to enhance the speed and accuracy of the loan approval process within banking environments.
Keywords	Loan approval, Machine learning,Cibil Score
Field	Mathematics > Statistics
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-28
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.43338
Short DOI	https://doi.org/g9mh7z

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