An Evaluation of Iranian Banking System Credit Risk: Neural Network and Logistic Regression

Author:
, ,
Year
: 2011
Abstract: 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.
Keyword(s): Credit allocation,neural network,multilayer perceptron,logistic regression
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