Predicting Bank Financial Failures Using Discriminant Analysis And Support Vector Machines Methods A Comparative Analysis In Commercial Banks In Sudan 2006-2014

Bank failures threaten the economic system as a whole. Therefore predicting bank financial failures is crucial to prevent andor lessen its negative effects on the economic system. Financial crises affecting both emerging markets and advanced countries over the centuries have severe economic consequences but they can be hard to prevent and predict identifying financial crises causes remains both science and art said Stijn Claessens assistant director of the International Monetary Fund. While it would be better to mitigate risks financial crises will recur often in waves and better crisis management is therefore important. Analyses of recurrent causes suggest that to prevent crises governments should consider reforms in many underlying areas. That includes developing prudent fiscal and monetary policies better regulating the financial sector including reducing the problem of too-big-to-fail banks and developing effective macro-prudential policies. Despite new regulations and better supervision crises are likely to recur in part because they can reflect deeper problems related to income inequality the political economy and common human behavior. As such improvements in crisis management are also needed. This is originally a classification problem to categorize banks as healthy or non-healthy ones. This study aims to apply Discriminant analysis and Support Vector Machines methods to the bank failure prediction problem in a Sudanese case and to present a comprehensive computational comparison of the classification performances of the techniques tested. Eleven financial and non-financial ratios with six feature groups including capital adequacy asset quality Earning and liquidity CAMELS are selected as predictor variables in the study. Credit risk also been evaluated using logistic analysis to study the effect of Islamic finance modes sectors and payment types used by Sudanese banks with regard to their possibilities of failure. Experimental results are evaluated using accuracy of prediction. Features selection has shown that new groups can be identified from CAMELS ratios and narrowing the data set space to 11 factors instead of eighteen. Discriminant analysis has identified 3 ratios with highest predictive power which are EAS Ratio of equity capital to total asset LADF Ratio of liquid assets to deposits and short term funds and RFR Rain Fall Ratio the later ratio is a novel one used for the first time by this research.