Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach
The aim of this study is to predict the impact of bank default risk on microcredit in Iran. Given the importance of microcredit in creating employment and reducing poverty and inequality, it is necessary to investigate the factors affecting access to these loans due to increased bank default risk. T...
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Mashhad: Behzad Hassannezhad Kashani
2024-12-01
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| Series: | International Journal of Management, Accounting and Economics |
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| Online Access: | https://www.ijmae.com/article_210440_80fc7271a9bdc9281a07b028fcbaab3a.pdf |
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| author | Reza Taheri Haftasiabi Yusef Mohammadzadeh Ameneh Naderi |
| author_facet | Reza Taheri Haftasiabi Yusef Mohammadzadeh Ameneh Naderi |
| author_sort | Reza Taheri Haftasiabi |
| collection | DOAJ |
| description | The aim of this study is to predict the impact of bank default risk on microcredit in Iran. Given the importance of microcredit in creating employment and reducing poverty and inequality, it is necessary to investigate the factors affecting access to these loans due to increased bank default risk. This study was conducted with a quantitative research approach and the data of all 28 Iranian banks in the period from 2017 to 2022 were analyzed. Machine learning tools, including artificial neural networks (ANN) and support vector machine (SVM), were used to analyze macroeconomic indicators such as GDP, inflation, exchange rate, interest rate, and financial variables of banks such as investment volume, amount of loans granted, total deposits, and bankruptcy risk indicators. The results showed that the increase in bankruptcy risk of banks has an inverse and significant relationship with the number of microloans. The developed models predicted this relationship with high accuracy and reliability, such that the accuracy of the ANN model was 100% and that of the SVM model was 95%. Thus, this study shows that the factors related to macroeconomic conditions and financial condition of banks serve as warning signals for the reduction of microcredit. On this basis, policy makers are recommended to focus on the financial stability of banks, increasing their resilience to economic shocks and reforming macroeconomic structures to avoid restricting access to microcredit and its negative impact on the economy. |
| format | Article |
| id | doaj-art-ef0c1ecd04b34bcbb4cf6c62693fc3e8 |
| institution | Kabale University |
| issn | 2383-2126 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Mashhad: Behzad Hassannezhad Kashani |
| record_format | Article |
| series | International Journal of Management, Accounting and Economics |
| spelling | doaj-art-ef0c1ecd04b34bcbb4cf6c62693fc3e82025-01-12T08:07:50ZengMashhad: Behzad Hassannezhad KashaniInternational Journal of Management, Accounting and Economics2383-21262024-12-0111121737175610.5281/zenodo.14538209210440Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence ApproachReza Taheri Haftasiabi0Yusef Mohammadzadeh1Ameneh Naderi2Faculty of Economics and Management, Urmia University, Urmia, IranFaculty of Economics and Management, Urmia University, Urmia, IranFaculty of Economics and Management, Urmia University, Urmia, IranThe aim of this study is to predict the impact of bank default risk on microcredit in Iran. Given the importance of microcredit in creating employment and reducing poverty and inequality, it is necessary to investigate the factors affecting access to these loans due to increased bank default risk. This study was conducted with a quantitative research approach and the data of all 28 Iranian banks in the period from 2017 to 2022 were analyzed. Machine learning tools, including artificial neural networks (ANN) and support vector machine (SVM), were used to analyze macroeconomic indicators such as GDP, inflation, exchange rate, interest rate, and financial variables of banks such as investment volume, amount of loans granted, total deposits, and bankruptcy risk indicators. The results showed that the increase in bankruptcy risk of banks has an inverse and significant relationship with the number of microloans. The developed models predicted this relationship with high accuracy and reliability, such that the accuracy of the ANN model was 100% and that of the SVM model was 95%. Thus, this study shows that the factors related to macroeconomic conditions and financial condition of banks serve as warning signals for the reduction of microcredit. On this basis, policy makers are recommended to focus on the financial stability of banks, increasing their resilience to economic shocks and reforming macroeconomic structures to avoid restricting access to microcredit and its negative impact on the economy.https://www.ijmae.com/article_210440_80fc7271a9bdc9281a07b028fcbaab3a.pdfartificial intelligencebankruptcy riskmachine learningmicrocredits |
| spellingShingle | Reza Taheri Haftasiabi Yusef Mohammadzadeh Ameneh Naderi Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach International Journal of Management, Accounting and Economics artificial intelligence bankruptcy risk machine learning microcredits |
| title | Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach |
| title_full | Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach |
| title_fullStr | Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach |
| title_full_unstemmed | Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach |
| title_short | Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach |
| title_sort | prediction of the impact of bank failure risk on micro credit in iran an artificial intelligence approach |
| topic | artificial intelligence bankruptcy risk machine learning microcredits |
| url | https://www.ijmae.com/article_210440_80fc7271a9bdc9281a07b028fcbaab3a.pdf |
| work_keys_str_mv | AT rezataherihaftasiabi predictionoftheimpactofbankfailureriskonmicrocreditinirananartificialintelligenceapproach AT yusefmohammadzadeh predictionoftheimpactofbankfailureriskonmicrocreditinirananartificialintelligenceapproach AT amenehnaderi predictionoftheimpactofbankfailureriskonmicrocreditinirananartificialintelligenceapproach |