Authors: D.K. Malhotra; Robert L. Nydick; Kunal Malhotra
Addresses: School of Business Administration, School House Lane and Henry Avenue, Thomas Jefferson University, Philadelphia, PA 19144, USA ' Management and Operations, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USA ' School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
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.
Keywords: bank failure; AI; support vector machines; SVM.
International Journal of Business Intelligence and Systems Engineering, 2017 Vol.1 No.2, pp.179 - 195
Received: 18 Feb 2017
Accepted: 03 Jul 2017
Published online: 11 Dec 2017 *