Designing a financing chain model under credit risk disorder

نویسندگان

1 Department of Accounting, Faculty of Economics and Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran

2 Assistant Professor of Accounting, Islamic Azad University Tehran South, Iran

3 Assistant Professor of Accounting, Islamic Azad University Tehran South, Iran

doi
10.30495/ijfma.2023.69696.1926
چکیده

Optimizing the financial flow in banks' financing chains against credit risk disorder is one of the important and fundamental demands of the owners of interests in banks,therefore,this research was conducted with the aim of designing a model of the financing chain under credit risk disorder in the keshavarzi Bank.The research is qualitative-quantitative.The statistical population in the qualitative part of the research is the senior managers of the credit department of the keshavarzi Bank in Tehran.The sampling is purposeful and by snowball method.The interview with ten of them showed theoretical saturation,but the interview with two other experts continued.Data collection was done by field method by conducting in-depth semi-structured interviews and the design of the model was coded with the foundation data method.Validity of the model was validated by double interaction and reliability by retest.In the quantitative part of the research,among the 320 managers of credit departments of the branches of excellent Bank in Tehran,175 of them were studied by a non-random method and according to the Morgan Chrissy table,with the researcher's questionnaire.For data analysis,factor analysis and SmartPLS software were used.Validity was checked withAVE and reliability withCR,and the fit of the model was accepted.Fully confirmed.This model introduces corporate banking and risk management as the main core of optimizing the financial supply chain under risk disruption.The comparison of this model with the current performance of the keshavarzi Bank shows that the proposed model with emphasis on advanced tools suitable for industry conditions It provides better optimization of the financing chain.