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

by   Sheikh Rabiul Islam, et al.

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.


page 1

page 2

page 3

page 4


Bond Default Prediction with Text Embeddings, Undersampling and Deep Learning

The special and important problems of default prediction for municipal b...

An Experimental Study of Class Imbalance in Federated Learning

Federated learning is a distributed machine learning paradigm that train...

The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression

Methods to correct class imbalance, i.e. imbalance between the frequency...

A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

The problem of class imbalance is extensive for focusing on numerous app...

Addressing the Real-world Class Imbalance Problem in Dermatology

Class imbalance is a common problem in medical diagnosis, causing a stan...

ReGrAt: Regularization in Graphs using Attention to handle class imbalance

Node classification is an important task to solve in graph-based learnin...

Preliminary Wildfire Detection Using State-of-the-art PTZ (Pan, Tilt, Zoom) Camera Technology and Convolutional Neural Networks

Wildfires are uncontrolled fires in the environment that can be caused b...

Please sign up or login with your details

Forgot password? Click here to reset