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