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Understanding and Forecasting of Credit Defaulters Using R- Programming |
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PP: 1439-1448 |
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Author(s) |
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V. Bhatt,
P. Pathak,
S. Rastogi,
Venkata M. Bhimavarapu,
P. Tapas,
S. Kadam,
N. Parashar,
P. Barge,
B. Agarwal,
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Abstract |
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| In Technology has provided numerous significant benefits to the financial industry. Financial transactions are now much smoother and faster than they were previously. Creditworthiness is a measure of how likely you are to repay your debt obligations, and it supports lenders decide whether or not to extend new credit to you. The current paper attempts to comprehend credit defaulters and develops a model to aid in understanding the determinants and prediction. A dataset of 376 responses was divided into training and testing data sets in proportions of 70% and 30%, respectively. The authors used traditional Binary Logistic Regression, Deep Learning, and Random Forest to achieve the empirical results. Logistic regression, an extension of linear regression with a categorical dependent variable, will also be used for comparison. IBM SPSS was used to run the binary logistic regression. and creates a model to aid in understanding the determinants and prediction.
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