Measuring the Determinants for the Survival of Indian Banks Using Machine Learning Approach
Banks’ survival is often seen as a crucial role in the financial system and the economy. Without a sound and effective banking system, no country can ever have a healthy economy. This research article concentrates on the analysis of collected data for the failed and surviving private and public bank...
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Veröffentlicht in: | FIIB business review 2019-03, Vol.8 (1), p.32-38 |
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description | Banks’ survival is often seen as a crucial role in the financial system and the economy. Without a sound and effective banking system, no country can ever have a healthy economy. This research article concentrates on the analysis of collected data for the failed and surviving private and public banks working in India. All possible bank-specific variables as well as macroeconomic and market structure variables have been used to understand the important factors accountable for the survival of the Indian banks through feature selection methods. The Output of the feature selection method shows that, the important factors for bank’s failure or survival are z-score, return on net worth, profit after tax, return on assets, equity, overheads, total assets, income, loan, inflation CPI, interest on revenue, liabilities and GDP growth. |
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The Output of the feature selection method shows that, the important factors for bank’s failure or survival are z-score, return on net worth, profit after tax, return on assets, equity, overheads, total assets, income, loan, inflation CPI, interest on revenue, liabilities and GDP growth.</description><identifier>ISSN: 2319-7145</identifier><identifier>EISSN: 2455-2658</identifier><identifier>DOI: 10.1177/2319714519825939</identifier><language>eng</language><publisher>New Delhi: Sage Publications India Private Ltd</publisher><subject>Artificial intelligence ; Assets ; Authorship ; Bank failures ; Banking industry ; Banking system ; Central banks ; Cognitive style ; Commercial banks ; Efficiency ; Equity ; Feature selection ; Financial institutions ; Financial systems ; GDP ; Gross Domestic Product ; Inflation ; Interest rates ; Liquid assets ; Loans ; Machine learning ; Market structure ; Principal components analysis ; Profitability ; Profits ; Prosperity ; Public sector ; Reserve requirements ; Survival analysis ; Taxation ; Variables</subject><ispartof>FIIB business review, 2019-03, Vol.8 (1), p.32-38</ispartof><rights>2019 Fortune Institute of International Business</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-2aadfc84af91ba70429299db93b03686f22fed71e8f6402d3dde67ed4beccae63</citedby><cites>FETCH-LOGICAL-c351t-2aadfc84af91ba70429299db93b03686f22fed71e8f6402d3dde67ed4beccae63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Shrivastav, Santosh Kumar</creatorcontrib><title>Measuring the Determinants for the Survival of Indian Banks Using Machine Learning Approach</title><title>FIIB business review</title><description>Banks’ survival is often seen as a crucial role in the financial system and the economy. 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subjects | Artificial intelligence Assets Authorship Bank failures Banking industry Banking system Central banks Cognitive style Commercial banks Efficiency Equity Feature selection Financial institutions Financial systems GDP Gross Domestic Product Inflation Interest rates Liquid assets Loans Machine learning Market structure Principal components analysis Profitability Profits Prosperity Public sector Reserve requirements Survival analysis Taxation Variables |
title | Measuring the Determinants for the Survival of Indian Banks Using Machine Learning Approach |
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