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Rangoonwala, Nikita
- The Legal Aspects of the Wilful Defaults in India:A Critical Study
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1 School of Business and Law, Navrachana University, Vadodara, Gujarat, IN
1 School of Business and Law, Navrachana University, Vadodara, Gujarat, IN
Source
International Journal of Banking, Risk and Insurance, Vol 7, No 2 (2019), Pagination: 76-85Abstract
Wilful Default entities are those who have the capacity but does not repay the loans, siphon off the fund, diverts the funds or sells off the mortgaged assets without approval of the banks. In March 2018, the proportion of wilful default in total NPA comprised 44%. Such worrisome figures reinforce the quest to study the issue in-depth; it is impelling to explore the phenomenon and figure out the reason behind such a brazen and rampant act of borrowing and wilfully not repaying. The objective is to find understand legal aspects of the wilful default. The study is descriptive in nature with content analysis from secondary sources like Insolvency and Bankruptcy Law, 2016, Companies Act, 2013, SARFAESI Act, 2002, The Recovery Of Debts Due To Banks And Financial Institutions Act, 1993, Securities and Exchange Board of India Act, and Master Circular of RBI. The research finds that various laws are concurrent to each other and have mild penalty. Only in Companies Act 2013 the penalty is harsher than others. Additionally, preventative measures for the banks have been suggested.Keywords
Non-Performing Assets, Wilful Default, Insolvency and Bankruptcy Code.References
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- Application of Artificial Neural Network to Predict Wilful Default for Commercial Banks in India
Abstract Views :118 |
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Authors
Affiliations
1 Navrachana University, Vadodara, Gujarat, IN
1 Navrachana University, Vadodara, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 8, No 2 (2020), Pagination: 13-22Abstract
Since 2014, the problem of the rise of Non-Performing Assets (NPA) in the Indian banking system has been a subject of investigation. A major impact of mounting NPA has tested the bank’s ability to recover bad loans and its capacity to lend in the short run. Among others, wilful default has been a significant category of NPA; it is detrimental to the financial health of the banking system. The share of wilful default in the total NPA for the year 2018 stands at 44%. By and large, wilful default indicates ‘intend of fraud’. As declared by various banks, around 106 companies are identified as wilful default companies from those listed between 2000 and 2018. The research paper constructs a model to predict the wilful default using an artificial neural network. The model is based on 106 wilful default companies and 106 non-default companies. The model predicts an accuracy rate of 92.2% and the variables with the highest degree of importance are found to be Profit before Interest and Taxes/Total Assets, followed by Enterprise Value/Total Assets, Operating Profit Margin, Cash Flow Financing/Cash Flow Investing, Total Debt/Total Asset, Sales/Capital Employed, Retained Earnings/Total Assets, Return on Shareholders’ Funds, PBIT/Sales, and others.Keywords
Artificial Neural Network, Wilful Default, Non-Performing Assets, Bankruptcy Prediction Model.References
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