Loan Eligibility Prediction Using Machine Learning

Gorantla Lavanya; Bobbala Naga Sunitha; Konkala Sai Kalpana; Ravinutala V P SaiViswanadh Sarma; B. Sravani; Nedunchezhian

Abstract

Banks and other financial institutions compete for customers by providing a wide range of services and products. Most banks, however, make the vast majority of their money from their credit portfolio. Loans accepted by borrowers might lead to interest charges. The loan portfolio, and customers' repayment habits in particular, can have a substantial impact on a bank's bottom line. The financial institution's Non-Performing Assets can be reduced if it can accurately predict which borrowers are likely to default on their loans. Therefore, there is substantial scholarly value in exploring the prediction of loan endorsement. In order to make accurate predictions, it is crucial to use Machine Learning methods. Based on a person's past loan qualification history, this research uses a machine learning methodology to predict the person's likelihood of consistently making loan repayments. The primary aim of this research is to foretell how likely it is that a given individual will be granted a loan.

Keywords

Loan, Machine Learning, Prediction, Testing, Training

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Cites this article as

G. Lavanya, B. N. Sunitha, K. S. Kalpana, R. V. P. S. Sarma, B. Sravani, Nedunchezhian, "Loan Eligibility Prediction Using Machine Learning", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, no.3, pp.403-405, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.3.64

Corresponding Author

Gorantla Lavanya

Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India

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