Articlediamond2015

Credit risk prediction: A comparative study between discriminant analysis and the neural network approach

39 citations0/0 files storedRePEc: Research Papers in Economics

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Financial Distress and Bankruptcy Prediction Banking stability, regulation, efficiency Forecasting Techniques and Applications

Abstract

Banks are concerned with the assessment of the risk of financial distress before giving out a loan. Many researchers proposed the use of models based on the Neural Networks in order to help the banker better make a decision. The objective of this paper is to explore a new practical way based on the Neural Networks that would help the banker to predict the non payment risk the companies asking for a loan. This work is motivated by the insufficiency of traditional prevision models. The sample consists of 86 Tunisian companies and 15 financial ratios were calculated, over the period from 2005 to 2007. The results were compared with those of discriminant analysis. They show that the neural networks technique is more accurate in term of predictability.

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References

1966Financial Ratios As Predictors of Failure 1977ZETATM analysis A new model to identify bankruptcy risk of corporations 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)2000Neural network credit scoring models 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 1996A comparison of neural networks and linear scoring models in the credit union environment 1993Recognizing Financial Distress Patterns Using a Neural Network Tool 2007A neural network approach for credit risk evaluation

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2021A predictive intelligence system of credit scoring based on deep multiple kernel learning 2019Credit Rating Forecasting Using Machine Learning Techniques 2018Credit Risk Prediction Using Artificial Neural Network Algorithm

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