Factors affecting SMEs credit risk and credit risk assessment based on blockchain-driven supply chain finance
<p>The purpose of this research is to examine the factors affecting SMEscredit risk and</p><p>credit risk assessment based on blockchain-driven supply chain finance. This research</p><p>mainly includes three objectives: The first...
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| Format: | thesis |
| Language: | eng |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=11095 |
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| Summary: | <p>The purpose of this research is to examine the factors affecting SMEscredit risk and</p><p>credit risk assessment based on blockchain-driven supply chain finance. This research</p><p>mainly includes three objectives: The first objective is to examine whether the</p><p>financing enterprises, core enterprises, assets position under financing, blockchain</p><p>platform and supply chain operation have significant impacts on credit risk by using</p><p>logistic regression and entropy method. The panel data were collected from CSMAR</p><p>on fifty-six SMEs, eight core enterprises and twenty-six blockchain enterprises in the</p><p>period of 2016-2020. The second objective is to establish a credit risk evaluation</p><p>index system and used factor analysis to extract the principal factors, then 11 factors</p><p>are extracted as the variable sources for credit risk assessment modeling. The third</p><p>objective is to build a credit risk assessment model by using five methods:</p><p>Classification Tree, Bagging algorithm, AdaBoost algorithm, Random Forest and</p><p>Logistic Regression to construct the credit risk assessment model. Then, according to</p><p>the model evaluation criteria, this research found out the credit risk assessment model</p><p>with the best prediction classification performance. The findings show that the</p><p>financing enterprises, core enterprises, assets position under finance, blockchain</p><p>platform, and supply chain operation have significant impacts on SMEscredit risk</p><p>when the confidence level is 90%. In general, the performance of AdaBoost algorithm</p><p>model is the best. It has the strongest ability to distinguish between enterprises with</p><p>credit risk and without credit risk, and has strong stability. The research not only</p><p>enriches the theories and method of credit risk assessment of SMEs, but also provides</p><p>assistance in solving the problem of financing difficulties for SMEs due to its ability</p><p>to accurately assess credit risk.</p> |
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