The financial health of the banking industry is an important prerequisite for economic stability and growth. As a consequence, the assessment of banks' financial condition is a fundamental goal for regulators. As on-site inspections are usually very costly, take a considerable amount of time and cannot be performed with high frequency, in order to avoid too frequent inspections without loosing too much information, supervisors also monitor banks' financial condition off-site. Typically, off-site supervision is based on different information available to supervisors, which includes mainly balance sheet and income statement data, data on the residual maturity of obligations, and credit register data about loans granted to individual borrowers above a given threshold.Off-site analysis uses different methods, such as CAMEL-based approaches, statistical techniques and credit risk models. Early warning systems based on statistical techniques reflect the rapidity with which the performance of a bank responds to a changing macroeconomic cycle, the conditions on the monetary and financial markets, and the interventions of the supervisory authority. Therefore, for the time being, statistical techniques like discriminant analysis and probit/logit regressions play a dominant role in off-site banking supervision. They allow an estimate to be made of the probability that a bank with a given set of characteristics will fall into one of two or more states, most often failure/non-failure, reflecting the bank's financial condition over an interval of time implied by the study design, usually defined as one year.An interesting academic discussion addresses the different advantages and disadvantages of statistical default prediction models as opposed to structural credit risk models. While statistical approaches do not explicitly model the underlying economic relationships, structural models emerge from corporate finance theory. However, there is ample empirical evidence that structural models perform poorly in predicting corporate bond spreads and corporate bankruptcy. Another problem in applying structural models to bank regulation is usually the lack of market data. For example, in the last decade in Austria only about 1% of a total of 1100 existing banks were listed on a stock exchange. Therefore, we focus on statistical bank default prediction in this paper.There is a quite extensive literature concerning the use of discriminant analysis and logit/probit regressions that distinguish between "good" and "bad" banks. Other statistical
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