The New Advanced Society

18An Extensive Survey on the Prediction of Bankruptcy

Sasmita Manjari Nayak and Minakhi Rout*

School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be) University, Bhubaneswar, India

Abstract

Prediction of bankruptcy is an active area for research which is associated with the state of insolvency where a company or a person is unable to repay the creditors the debt amount. Up to now so many statistical and machine learning based models have been introduced for bankruptcy prediction. The pre-processing phase is an important step to enhance the performance of the model. Thus, one needs to choose effective pre-processing techniques which can be more suitable for the data set considered. So, this chapter focused on both the model, specifically, ensemble models for classification to address how new improved models are developed by combining two or more simple developed techniques and pre-processing techniques in order to address the imbalanced nature of the data and outlier if any present in the data. In most of the chapters the authors make some comparisons to show up the performance of their models with some other previously developed models. Here, we observed from the survey that the pre-processed datasets give better prediction outcome and it also proved that the ensemble models are more powerful for bankruptcy prediction as compared to the single models.

Keywords: Bankruptcy prediction, ensemble models, imbalance dataset, outlier, pre-processing, ...