Framework to predict NPA/Willful defaults in corporate loans: a big data approach

Attigeri, Girija V and Pai, Manohara M.M. and Pai, Radhika M (2019) Framework to predict NPA/Willful defaults in corporate loans: a big data approach. International Journal of Electrical and Computer Engineering, 9 (5). pp. 3786-3797. ISSN 2088-8708

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Abstract

Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework.

Item Type: Article
Uncontrolled Keywords: Big Data Corporate Loan Financial Fraud Machine Learning Non Performing Assets Parameterization Willful default
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 06 Mar 2021 05:22
Last Modified: 06 Mar 2021 05:22
URI: http://eprints.manipal.edu/id/eprint/156481

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