Efficacies of artificial neural networks ushering improvement in the prediction of extant credit risk models

The study’s objective is to check whether the predictive power of Machine Learning Techniques is better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model as a default driver. We considered the covid pandemic a black swan event that had caused ambiguity. A significant factor that has increased the probability of bankruptcy in recent times has been the large-scale supply chain disruptions and crippling lockdowns. Firms are trying to get back to pre-Covid utilization of plant capacity or pivot their business models differently to seize newer opportunities amidst the crisis. We considered the change in operating expenditure (primarily decrease) as our proxy for uncertainty as firms were forced to cut down majorly on their operations and thus incurred lesser variable costs. In an economy showing inflationary trends, the operating expenses will generally increase. But we found that the operational costs had shown a dip in the case of many of the firms during FY 20–21, and we attributed it to Covid disruptions. Results show that Machine Learning Techniques are better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model.