Abstract
Financial institutions suffer from the risk of losing money from bad customers, specifically banking sectors, where the risk of losing money is higher, due to bad loans. This causes an economic slowdown of the nation. The banking industry has a significant action of lending cash to individuals who are needing cash. In order to payback, the principal borrowed from the depositor bank collects the interest made by the principal borrowers. Credit risk investigation is turning into a significant field in financial risk management. Many credit risk analysis strategies are utilized for the assessment of credit risk of the client dataset. In this paper, we designed a model which takes loan data of the customers who applied for a loan from a bank and predicted to give the credit of the client or reject the utilization of the client. The proposed model takes the factors which affect the loan status of a person, thus providing accurate results for issuing credit to the client or reject the utilization of the client by considering all possibilities.
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Srinivasa Rao, M., Sekhar, C., Bhattacharyya, D. (2021). Comparative Analysis of Machine Learning Models on Loan Risk Analysis. In: Bhattacharyya, D., Thirupathi Rao, N. (eds) Machine Intelligence and Soft Computing. Advances in Intelligent Systems and Computing, vol 1280. Springer, Singapore. https://doi.org/10.1007/978-981-15-9516-5_7
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DOI: https://doi.org/10.1007/978-981-15-9516-5_7
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