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
1. Verfasser: Shrivastav, Santosh Kumar
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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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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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