Using a Bayesian Belief Network for Going-Concern Risk Evaluation

An auditor\u27s verdict on client\u27s financial health is delivered in the form of a going concern (GC) opinion. Although an auditor is not required to predict the financial future of a client, stakeholders take the GC opinion as a guideline on a company\u27s financial health. The GC opinion has been a subject of much debate in the financial literature, as it is one of the most widely read parts of an audit report. Researchers and academicians believe that auditors have made costly mistakes in rendering GC opinions. Several factors have been identified as the root causes for these mistakes, including growing business complexities, insufficient auditor training, internal and external pressures, personal biases, economic considerations, and fear of litigation. To overcome these difficulties, researchers have been trying to devise effective audit tools to help auditors form accurate GC opinions on clients \u27 financial future. Introduction of ratio-based bankruptcy models using a variety of statistical techniques are attempts in the right direction. The results of such efforts, though not perfect, are encouraging. This study examined several popular ratio-based statistical models and their weaknesses and limitations. The author suggests a new model based on the robust Bayesian Belief Network (BBN) technique. Based on sound Bayesian theory, this model provides remedies against the reported deficiencies of the ratio-based techniques. The proposed system, instead of comparing a company\u27s financial ratios with the industrywide ratios, measures the internal financial changes within a company during a particular year and uses the changing financial pattern to predict the financial viability of the company. Unlike other popular models, the proposed model takes various qualitative factors into consideration before delivering the GC verdict. The proposed system is verified and validated by comparing its results with the industry de facto Z-score model