Estimating Default Probability of Bank Customers Using Neural Networks Method (Case Study: Pasargad Bank)
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 investigati...
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Iran Banking Institute
2018-02-01
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| Series: | مطالعات مالی و بانکداری اسلامی |
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| Online Access: | http://jifb.ibi.ac.ir/article_58627_fa2cc7065aca90f46e521b1d36589f57.pdf |
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doaj-53ac9475f80d4f5aaa1a358c5b6458f12020-11-24T21:37:00ZfasIran Banking Instituteمطالعات مالی و بانکداری اسلامی2588-35692588-44332018-02-013پاییز و زمستان12358627Estimating Default Probability of Bank Customers Using Neural Networks Method (Case Study: Pasargad Bank)Mohammad Hossein Pourkazemi0Eldar Sedaghat Parast1Reza Dehpanah2Faculty Member, Shahid Beheshti UniversityFaculty Member, Iran Banking InstituteM. A in Islamic BankingThe 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.http://jifb.ibi.ac.ir/article_58627_fa2cc7065aca90f46e521b1d36589f57.pdfForecastingCredit riskData miningNeural Network |
| collection |
DOAJ |
| language |
fas |
| format |
Article |
| sources |
DOAJ |
| author |
Mohammad Hossein Pourkazemi Eldar Sedaghat Parast Reza Dehpanah |
| spellingShingle |
Mohammad Hossein Pourkazemi Eldar Sedaghat Parast Reza Dehpanah Estimating Default Probability of Bank Customers Using Neural Networks Method (Case Study: Pasargad Bank) مطالعات مالی و بانکداری اسلامی Forecasting Credit risk Data mining Neural Network |
| author_facet |
Mohammad Hossein Pourkazemi Eldar Sedaghat Parast Reza Dehpanah |
| author_sort |
Mohammad Hossein Pourkazemi |
| title |
Estimating Default Probability of Bank Customers
Using Neural Networks Method
(Case Study: Pasargad Bank) |
| title_short |
Estimating Default Probability of Bank Customers
Using Neural Networks Method
(Case Study: Pasargad Bank) |
| title_full |
Estimating Default Probability of Bank Customers
Using Neural Networks Method
(Case Study: Pasargad Bank) |
| title_fullStr |
Estimating Default Probability of Bank Customers
Using Neural Networks Method
(Case Study: Pasargad Bank) |
| title_full_unstemmed |
Estimating Default Probability of Bank Customers
Using Neural Networks Method
(Case Study: Pasargad Bank) |
| title_sort |
estimating default probability of bank customers
using neural networks method
(case study: pasargad bank) |
| publisher |
Iran Banking Institute |
| series |
مطالعات مالی و بانکداری اسلامی |
| issn |
2588-3569 2588-4433 |
| publishDate |
2018-02-01 |
| description |
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. |
| topic |
Forecasting Credit risk Data mining Neural Network |
| url |
http://jifb.ibi.ac.ir/article_58627_fa2cc7065aca90f46e521b1d36589f57.pdf |
| work_keys_str_mv |
AT mohammadhosseinpourkazemi estimatingdefaultprobabilityofbankcustomersusingneuralnetworksmethodcasestudypasargadbank AT eldarsedaghatparast estimatingdefaultprobabilityofbankcustomersusingneuralnetworksmethodcasestudypasargadbank AT rezadehpanah estimatingdefaultprobabilityofbankcustomersusingneuralnetworksmethodcasestudypasargadbank |
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