Evaluating bank solvency with support vector machines
by D.K. Malhotra; Robert L. Nydick; Kunal Malhotra
International Journal of Business Intelligence and Systems Engineering (IJBISE), Vol. 1, No. 2, 2017

Abstract: Banks as financial intermediaries play a very useful role in economic growth by facilitating the flow of funds to various sectors of the economy. Deterioration in a bank's performance and potential failure of the bank may lead to loss of confidence in the financial system that can result in loss of household savings and non-availability of funds to the business sector for economic expansion and growth. Banking regulators around the world are always looking for ways to identify sooner the banks that can be at risk of failure so that corrective action can be taken with minimal disruption to the economy. This study illustrates the use of support vector machines, an artificial intelligence technique, to predict the pending insolvency of a bank so that regulators can take appropriate steps to prevent a 'domino effect'. The study also compares the performance of support vector machines to multiple discriminant analysis in identifying 'unsafe' banks. To alleviate the problem of bias in the training set and to examine the robustness of support vector machine classifiers in identifying unsafe banks, we cross-validate our results through seven different samples of the data.

Online publication date: Thu, 14-Dec-2017

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