Predicting the credit risk of small and medium‐sized enterprises in supply chain finance using machine learning algorithms

Abstract

The credit risk of small and medium‐sized enterprises (SMEs) in supply chain finance (SCF) hinders the sustainability of the supply chain and threatens the monetary loss of SCF partners, that is, focal enterprises (FEs) and financial institutions (FIs). Thus, it is crucial to predict SMEs' credit risk accurately. However, redundant features and imbalanced sample data may decrease the accuracy of the prediction model. Therefore, a novel credit risk prediction framework is proposed called FS‐RS‐ML, which is short for Feature Selection–Resample Strategy–Machine Learning. In this approach, Recursive Feature Elimination with Cross‐Validation (RFECV) is applied to select key features influencing SMEs' credit risk prediction, and three resampling techniques are used to balance the sample data. Finally, five machine learning algorithms are adopted to classify high‐risk and low‐risk SMEs. The experiments based on Chinese SCF data demonstrate that the FS‐RS‐ML framework outperforms any single algorithm. Moreover, according to the analysis of empirical results, features derived from SMEs have the most important impact on credit risk prediction. And the effect of key features on predicting SMEs' credit risk is revealed.

Blockchain and Supply-Chain Financing: An Evolutionary Game Approach with Guarantee Considerations

“…This asymmetry can lead to trust deficits and suboptimal decision-making, potentially undermining the collaborative nature of supply-chain relationships. To mitigate this, researchers have explored the use of advanced analytical techniques, such as machine learning and data analytics, to better assess credit risks and improve the transparency of financial transactions within the supply chain [7,17].…”

“…The payoffs for adhering to agreements and defaulting of the SME, respectively, denoted as E y and E (1−y) : (7) and the average profit in both cases is…”

“…Despite the transformative potential of SCF, traditional financing modes are often hindered by a myriad of challenges that detract from their effectiveness. One of the most significant challenges is information asymmetry, which can lead to adverse selection and moral hazard, thereby escalating the risk profile for financiers [6,7]. This asymmetry stems from the lack of transparency in the operational dealings and financial standings of SMEs, making it onerous for lenders to accurately assess the creditworthiness and concomitant risks associated with extending credit facilities.…”