Summary 

Bankruptcy forecasting models in financial risk analysis have become an important decision support tool for stakeholders in organizations, including auditors, lenders, and shareholders. It has been shown that naive Bayesian networks perform well in predicting bankruptcy. The presented model relies on these results and uses a naive Bayesian network.

Various methods have been introduced in the development of bankruptcy prediction models. Including probabilistic models used to provide early warning of a company’s bankruptcy. I will consider two different probabilistic models, one that is simpler and makes more assumptions, and the other that is somewhat more complex but makes fewer assumptions. Both models can make accurate predictions with the help of historical data to estimate the probable probabilities. In particular, it was found that the more complex model is very well calibrated to estimate the probabilities. It has been shown that naive Bayesian networks perform well in predicting bankruptcy. The presented model relies on these results and uses a naive Bayesian network. I think such a model is a prerequisite for making a useful decision in the auditor's decision making   process.