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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| The purpose of this research is to examine the factors affecting SMEs’credit risk and
credit risk assessment based on blockchain-driven supply chain finance. This research
mainly includes three objectives: The first objective is to examine whether the
financing enterprises, core enterprises, assets position under financing, blockchain
platform and supply chain operation have significant impacts on credit risk by using
logistic regression and entropy method. The panel data were collected from CSMAR
on fifty-six SMEs, eight core enterprises and twenty-six blockchain enterprises in the
period of 2016-2020. The second objective is to establish a credit risk evaluation
index system and used factor analysis to extract the principal factors, then 11 factors
are extracted as the variable sources for credit risk assessment modeling. The third
objective is to build a credit risk assessment model by using five methods:
Classification Tree, Bagging algorithm, AdaBoost algorithm, Random Forest and
Logistic Regression to construct the credit risk assessment model. Then, according to
the model evaluation criteria, this research found out the credit risk assessment model
with the best prediction classification performance. The findings show that the
financing enterprises, core enterprises, assets position under finance, blockchain
platform, and supply chain operation have significant impacts on SMEs’credit risk
when the confidence level is 90%. In general, the performance of AdaBoost algorithm
model is the best. It has the strongest ability to distinguish between enterprises with
credit risk and without credit risk, and has strong stability. The research not only
enriches the theories and method of credit risk assessment of SMEs, but also provides
assistance in solving the problem of financing difficulties for SMEs due to its ability
to accurately assess credit risk. |
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