An Innovative Approach to Predict Bankruptcy


EOI: 10.11242/viva-tech.01.02.08

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Citation

Mihir H. Panchal, Mayur B. Bodar, Sunny R. Maurya, Tatwadarshi P. N., "An Innovative Approach to Predict Bankruptcy", VIVA-IJRI Volume 1, Issue 2, Article 8, pp. 1-6, 2019. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

Bankruptcy is a legal status of a person or other organization that cannot repay their debts to creditors. Bankruptcy prediction is the task of predicting bankruptcy and by doing various surveys we can avoid financial distress of firms. It is a huge area of accounting and finance research. The significance of this area is an important part of financial specialists and creditors in assessing the probability that a firm may go bankrupt or not. Estimating the risk of corporate bankruptcies is very important as the effect of bankruptcy is on a global level. The aim of predicting financial distress is to develop a predictive model that combines various economic factors which allow foreseeing the financial status of a firm. In this domain, various methods were proposed that were based on neural networks, Support Vector Machines, Decision Trees, Random Forests, Naïve Bayes, Balanced Bagging and Logistic Regression. In this paper, we document our observations as we explore and build a Restricted Boltzmann Machine to Bankruptcy Prediction. We started by carrying out data pre-processing where we impute the missing data values using Mean Imputation. To solve the data imbalance issue, we apply the Synthetic Minority Oversampling Technique (SMOTE) to oversample the minority class labels. Finally, we analyze and evaluate the performance of the model.

Keywords

Artificial Neural Network, Decision Trees, Logistic Regression, Naïve Bayes, Random Forests, Restricted Boltzmann machine, Support Vector Machine.

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