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In the area of credit risk analytics, current Bankruptcy Prediction Models(BPMs) struggle with (a) the availability of comprehensive and real-world datasets and (b) the presence of extreme class imbalance in the data (i.e., veryfew samples for the minority class) that degrades the performance of theprediction model. Moreover, little research has compared the relativeperformance of well-known BPM's on public datasets addressing the classimbalance problem. In this work, we apply eight classes of well-known BPMs, assuggested by a review of decades of literature, on a new public dataset namedFreddie Mac Single-Family Loan-Level Dataset with resampling (i.e., addingsynthetic minority samples) of the minority class to tackle class imbalance.Additionally, we apply some recent AI techniques (e.g., tree-based ensembletechniques) that demonstrate potentially better results on models trained withresampled data. In addition, from the analysis of 19 years (1999-2017) of data,we discover that models behave differently when presented with sudden changesin the economy (e.g., a global financial crisis) resulting in abruptfluctuations in the national default rate. In summary, this study should aidpractitioners/researchers in determining the appropriate model with respect todata that contains a class imbalance and various economic stages.

Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy