Use of Soft Computing Techniques in Credit Risk Management of an Indian Bank: Application of Artificial Neural network

Abhijit Dutta, A. Barman
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Abstract

This study uses ten variables from an Indian commercial bank in India for its retail customers. The study uses a Multilayer Perceptron (MLP) - Artificial Neural network architecture to understand the usability of such data for credit risk management in India. The results are encouraging and show high level of predictability, low bias while iterating the information. This study shows that information such as income level and default to ratio of CIBIL score are highly usable information by Banks to understand the default in commercial banks. The study also shows that the use of ANN yield good predictable result and can be used for credit risk management of a bank. The model is being able to learn properly and the results are consistent which mean that such a technique can be used in the long run for the credit risk management of a bank.
软计算技术在印度某银行信用风险管理中的应用:人工神经网络的应用
本研究使用了印度一家商业银行在印度的零售客户的十个变量。该研究使用多层感知器(MLP) -人工神经网络架构来了解此类数据在印度信用风险管理中的可用性。结果令人鼓舞,在迭代信息时显示出高水平的可预测性和低偏差。本研究表明,收入水平和CIBIL得分的违约比率等信息是银行了解商业银行违约的高度有用的信息。研究还表明,人工神经网络的使用产生了良好的可预测结果,可以用于银行的信用风险管理。该模型能够正确地学习,并且结果是一致的,这意味着这种技术可以长期用于银行的信用风险管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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