A integrated hybrid fuzzy multiple-criteria decision-making model for non-performing Loans collections in the banking system (Case study: Shahr Bank)
محورهای موضوعی : Multi-Criteria Decision Analysis and its Application in Financial Managementkiamars fathi 1 , Majid Rashidi 2 , Mahmoud Modiri 3 , Sayedeh Mahboubeh Jafari 4
1 - Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial management, Kish International Branch, Islamic Azad University, Kish Island, Iran
3 - Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Non-performing loans, Fuzzy multiple-criteria decision-making, Bank,
چکیده مقاله :
The increase in non-performing loans considerably reduces profit in the banking systems. To solve these problems, factors influencing the collection of non-performing loans have to be examined in a hopeful attempt to reduce it to an ac-ceptable level. The present study is conducted to analyze and study factors influ-encing the collection of non-performing loans in Shahr Bank. This research is an applied study regarding its goal. It was conducted in two sections, namely the qualitative and quantitative sections. In the qualitative sections, the factors influ-encing the collection of receivables were identified using the theoretical literature and interviews with senior managers of Shahr Bank through the encoding process. In the quantitative section, data was collected by surveying the opinions of 12 experts, including senior managers of Shahr Bank in 2020 using a questionnaire. Thereafter, the factors were selected using the fuzzy Delphi technique and the relationships between them were determined using the fuzzy DEMATEL method. Finally, the factors were weighted and prioritized using the fuzzy ANP method. The research findings showed that non-performing loans can be collected through six types of factors including organizational, regulatory, customer, banking, envi-ronmental, and operational factors. The environmental factor is the most influential factor, while the operational factor is the most influenced and most important factor. In addition, behavioural, contextual, and structural sub-factors have the highest level of importance in the collection of non-performing loans in the order mentioned. These findings can help bank managers make decisions to improve the collection of receivables.
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