Nasser Mohammadi

Work place: Department of computer engineering, Tehran Science and Research Branch, Islamic Azad University, Iran



Research Interests: Data Mining, Database Management System, Data Structures and Algorithms


Nasser Mohammadi received the BS and MS degrees in Computer engineering from Parand Azad University, Iran, in the year 2010 and Tehran Science and Research University, Iran, in the year 2014, respectively. His research interests include business intelligence, data mining, and system analysis and system design.

Author Articles
Customer Credit Risk Assessment using Artificial Neural Networks

By Nasser Mohammadi Maryam Zangeneh

DOI:, Pub. Date: 8 Mar. 2016

Since the granting of banking facilities in recent years has faced problems such as customer credit risk and affects the profitability directly, customer credit risk assessment has become imperative for banks and it is used to distinguish good applicants from those who will probably default on repayments. In credit risk assessment, a score is assigned to each customer then by comparing it with the cut-off point score which distinguishes two classes of the applicants, customers are classified into two credit statuses either a good or bad applicant. Regarding good performance and their ability of classification, generalization and learning patterns, Multi-layer Perceptron Neural Network model trained using various Back-Propagation (BP) algorithms considered in designing an evaluation model in this study. The BP algorithms, Levenberg-Marquardt (LM), Gradient descent, Conjugate gradient, Resilient, BFGS Quasi-newton, and One-step secant were utilized. Each of these six networks runs and trains for different numbers of neurons within their hidden layer. Mean squared error (MSE) is used as a criterion to specify optimum number of neurons in the hidden layer. The results showed that LM algorithm converges faster to the network and achieves better performance than the other algorithms. At last, by comparing classification performance of neural network with a number of classification algorithms such as Logistic Regression and Decision Tree, the neural network model outperformed the others in customer credit risk assessment. In credit models, because the cost that Type II error rate imposes to the model is too high, therefore, Receiver Operating Characteristic curve is used to find appropriate cut-off point for a model that in addition to high Accuracy, has lower Type II error rate.

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