Estimating Default Probability of Bank Customers Using Neural Networks Method (Case Study: Pasargad Bank)

• Main Authors: Mohammad Hossein Pourkazemi, Eldar Sedaghat Parast, Reza Dehpanah
• Format: Article
• Language: fas
• Published: Iran Banking Institute 2018-02-01
• Series: مطالعات مالی و بانکداری اسلامی
• Subjects: Forecasting; Credit risk; Data mining; Neural Network
• Online Access: http://jifb.ibi.ac.ir/article_58627_fa2cc7065aca90f46e521b1d36589f57.pdf
• ISSN: 2588-3569; 2588-4433

The purpose of this study is identifying factors affecting the probability of loan default and forecasting default probability of non-corporate (natural) customers of Pasargad bank by means of neural networks method (NNM). Variables influencing creation of default were identified through investigating background studies and literature review. At the next step, data related to 470 customers were collected from a statistical population of 25342 people who received loans from Pasargad bank in Tehran region from 2013 to 2014. Results show that NNM could accurately forecast 92% of applicants default probability. According to NNM results, bad financial history or type of collateral have had more significant effect on default probability than the other input variables.