Industrial and Systems Engineering https://aurora.auburn.edu/handle/11200/44258 2026-04-15T12:18:39Z 2026-04-15T12:18:39Z A Bayesian Approach to Detect the Firms with Material Weakness in Internal Control https://aurora.auburn.edu/handle/11200/49436 2019-08-01T15:43:58Z A Bayesian Approach to Detect the Firms with Material Weakness in Internal Control Capturing of relevant patterns in company’s financial data and the implications on the reporting are important for various financial statement users to identify the triggers of the significant deficiencies and material weaknesses. The objective of this study is to construct a company-specific risk score for the companies’ internal weaknesses, as well as to uncover the conditional relations between the independent predictors of firms’ material weaknesses. To do so, Tree Augmented Naive Bayes (TAN) and Logistic Regression (LR) algorithms are employed to analyze the data obtained from COMPUSTAT (Research Insight) for one year before the Material Weakness in Internal Control (MWIC) disclosure on several operating and financial ratios such as total asset turnover, profitability, capital intensity, size, current ratio, and operating performance. The proposed TAN method provides novel information on the interactions among the predictors and the conditional probability of MWIC for a given set of relevant firm characteristics.