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Banking system crises and bank failures can have painful impacts. Being an Islamic Bank is riskier than being a conventional bank. As IBIs risk management emphasizes risk-sharing, conventional banks transfer their risk to borrowers. In our study, we have taken 4 risk factors which are Rate of Return Risk, Operational Risk, Credit Risk and Liquidity Risk, as a proxy of all the risks faced by IBIs and developed a Composite Risk Index using these major risk indicators of Islamic banks. Further, this study has proposed indicators like Size1 for liquid assets and Size2 for fixed assets, as in Islamic banks, there is no concept of money markets. The data for the research was collected from the secondary sources of Islamic banks’ financial statements, the EIKON database and World development indicators. The data for 96 Islamic banks were analyzed. The data was analyzed through Python, R and Stata. This study is instrumental in using the unsupervised machine learning clustering algorithm known as KMeans to estimate the prevalent risk level of the bank by aggregating four-dimensional risk indicators to form a single indicator. Moreover, the study explored macroeconomic determinants i.e., Asymmetric Stock Market, Real Business Cycle (GDP), Regulatory Quality and Economic Globalization of the Composite Risk Index and developed a channel via which it affects the risk level of banks. The results showed that in a country, better regulatory quality, higher fixed assets and economic boom in the business cycle play an important role in reducing the risk level of Islamic banks. This study provides a quantified contribution of Islamic banking to social welfare, compared to focusing on the numbers of risk indicators only and comparing it with fixed boundaries developed for the case other indicators.
Raza et al. (Sat,) studied this question.