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- W4378570501 abstract "Abstract: A method of data analysis called machine learning automates the creation of analytical models. It is an area of artificial intelligence based on the idea that robots can learn from data, recognise patterns, and form opinions with little help from people. One of the most important and difficult tasks facing banks is the evaluation of credit risk. To prevent financial loss, accuracy is crucial in the classification of credit data. Lending money to people who need it is a major operation in the banking sector. The depositor bank receives the interest earned by the main borrowers in order to repay the principal borrowed from them. Financial risk management is increasingly focusing on credit risk analysis. The ability to estimate credit risk, monitor it, trust the model, and process loans well are essential for decision-making and transparency. In this study, we develop binary classifiers based on machine learning and deep learning models on actual data to forecast the likelihood of loan default. In the aforementioned research, we examine various methods for credit risk analysis that are used to the assessment of credit risk datasets." @default.
- W4378570501 created "2023-05-28" @default.
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- W4378570501 date "2023-05-31" @default.
- W4378570501 modified "2023-10-14" @default.
- W4378570501 title "Credit Risk Analysis Using Naive Bayes in Machine Learning" @default.
- W4378570501 doi "https://doi.org/10.22214/ijraset.2023.52943" @default.
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