Prediction of Banking Credit Risk Using Logistic Regression and The Artificial Neural Network Models: A Case Study of English Banks (original) (raw)
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International Journal of Applied Operational Research - An Open Access Journal, 2015
One of the most important issues always facing banks and financial institutes is the issue of credit risk or the possibility of failure in the fulfillment of obligations by applicants who are receiving credit facilities. The considerable number of banks’ delayed loan payments all around the world shows the importance of this issue and the necessary consideration of this topic. Accordingly, many efforts have been made for providing an efficient model for more accurate evaluation and classification of applicants receiving credit facilities for valid decision making about granting or not granting these facilities to them. Different statistic methods have been applied for this purpose, such as Discriminant Analysis, Probit Regression, Logistic Regression, Neural Network and so on. Among these methods, Neural Network has been considered mostly because of its high flexibility in recent years. In this research, many efforts have been made to examine the efficiency of Logistic Regression an...
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International Journal of Physical Sciences, 2011
The current study seeks to provide a new solution for evaluation of banking system customers risk by integrating different scientific methodology. Evaluation of banking system customers risk in Iranian banks relies on experts judgment and fingertip rule. This type of evaluation resulted in high rate of postponed claims; therefore, designing new intelligent model for credit risk evaluation will be helpful, thus in this paper, we formulated an intelligent model by neural network and logistic regression that evaluated all individual customers credit risk without prejudice and discrimination. The result revealed that neural network and logistic regression have the same ability in predicting customer credit risk. Their ability in customer credit risk correct evaluation was nearly 79.50%. We suggested that both models could be used by all financial system as consultant model for customer credit risk prediction. The study also involved only one banking system credit customers, which concerns just Tehran city customers and its sample includes only individual customers, thus cannot be for institutional customers. Offering a case study, this paper presents a guide for banking system to predict any customer credit risk and regulate any customer loan in the light of customer risk that was extracted by neural network, and logistic regression employed different scientific methodologies in their service quality development efforts. Intending to offer scientific approaches to risk evaluation as a tool of customer credit risk assessment in banking system loan allocation procedures, this paper tries to bridge the current gap between academicians and practitioners; adds to the relatively limited theoretical literature.
Credit Risk Analysis Applying Logistic Regression, Neural Networks and Genetic Algorithms Models
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Using the artificial neural network to assess bank credit risk: a case study of Indonesia
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Ever since the Asian Financial Crisis, concerns have risen over whether policymakers have sufficient tools to maintain financial stability. The ability to predict financial disturbances enables the authorities to take precautionary action to minimize their impact. In this context, the authorities may use any financial indicators which may accurately predict shifts in the quality of bank exposures. This paper uses key macro-economic variables (i.e. GDP growth, the inflation rate, stock prices, the exchange rates, and money in circulation) to predict the default rate of the Indonesian Islamic banks' exposures. The default rates are forecasted using the Artificial Neural Network (ANN) methodology, which incorporates the Bayesian Regularization technique. From the sensitivity analysis, it is shown that stock prices could be used as a leading indicator of future problem. . 5 Under a bank run, the depositors are paid out on a first-come-first-served basis.
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International Journal of Management Science and Engineering Management, 2012
The aim of this paper is to compare the model of logistic regression versus logistic regression with random effects in order to predict the credit risk of Tunisian banks. To do this, a battery of 26 ratios was calculated from balance sheets and income statements of 528 Tunisian firms from different sectors of activities for the fiscal years 1999-2006. By using information about the activity sector of each firms, we applied the logistic regression model with random effects to take into account the presence of unobserved heterogeneity. The obtained results show that the integration of sectoral effect improves the quality of model predictions in terms of good classification as well as by the ROC curve results.