A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction

Sunitha Devi · Y. Radhika

70 citations0/0 files storedInternational Journal of Machine Learning and Computing

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Artificial Intelligence in Healthcare

Abstract

The bankruptcy prediction model (BPM) is eminently essential for financial institutions to verify the creditworthiness of companies or management.The inability to accurately predict the bankruptcy can destroy the effects of socio-economics.Hence, it is significant to offer financial decision makers with efficient bankruptcy prediction to forestall these loss states.This paper presents a comprehensive review based on various statistical and machine learning techniques to address the issue of bankruptcy prediction.The statistical techniques include, linear discriminant analysis (LDA), multivariate discriminant analysis (MDA) and logistic regression (LR), and machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) and decision trees.Traditional statistical techniques were used to handle large data sets without affecting the prediction performance.Furthermore, the machine learning techniques provide greater prediction accuracy than the traditional statistical techniques for smaller data sets.Besides, optimization techniques, such as genetic algorithm (GA) and particle swarm optimization (PSO), were integrated with machine learning techniques to further improve the prediction accuracy for large data sets.This paper conducts a comparative analysis of the various techniques used based on their corresponding benefits and limitations.In our future work, the prediction of bankruptcy may be improved by integrating other heuristic evolutionary algorithms with the machine learning techniques using the Apache Mahout tool.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)2004An application of support vector machines in bankruptcy prediction model 2006Predicting corporate bankruptcy: where we stand?2002A genetic algorithm application in bankruptcy prediction modeling 2005Hybrid genetic algorithms and support vector machines for bankruptcy prediction 2011Machine Learning in Financial Crisis Prediction: A Survey

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