  In recent years, bankruptcy forecasting has gained lot of attention from
researchers as well as practitioners in the field of financial risk management.
For bankruptcy prediction, various approaches proposed in the past and
currently in practice relies on accounting ratios and using statistical
modeling or machine learning methods. These models have had varying degrees of
successes. Models such as Linear Discriminant Analysis or Artificial Neural
Network employ discriminative classification techniques. They lack explicit
provision to include prior expert knowledge. In this paper, we propose another
route of generative modeling using Expert Bayesian framework. The biggest
advantage of the proposed framework is an explicit inclusion of expert judgment
in the modeling process. Also the proposed methodology provides a way to
quantify uncertainty in prediction. As a result the model built using Bayesian
framework is highly flexible, interpretable and intuitive in nature. The
proposed approach is well suited for highly regulated or safety critical
applications such as in finance or in medical diagnosis. In such cases accuracy
in the prediction is not the only concern for decision makers. Decision makers
and other stakeholders are also interested in uncertainty in the prediction as
well as interpretability of the model. We empirically demonstrate these
benefits of proposed framework on real world dataset using Stan, a
probabilistic programming language. We found that the proposed model is either
comparable or superior to the other existing methods. Also resulting model has
much less False Positive Rate compared to many existing state of the art
methods. The corresponding R code for the experiments is available at Github
repository.
