We have a high-quality paper published in Annals of Operation Research (AnOR, CABS 3, Q1, ABDC A). It's FREE FOREVER.
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Banks, financial services and businesses need data analytics to understand the risk in detail, e.g., where and how, and ways to minimize it. We can know where they can improve, and even identify strategies to reach their targets. We've developed innovative methods for analysis and predictions.
This video has been created to help explain its significance and contributions we have made. Please cite or consider citing if you find it relevant, useful or inspiring! Many thanks.
Abstract
The control of credit risk is an important topic in the development of supply chain finance. Financial service providers should distinguish between low- and high-quality customers to predict credit risk accurately. Proper management of credit risk exposure contributes to the long-term viability and profitability of banks, systemic stability, and efficient capital allocation in the economy. Moreover, it benefits the development of supply chain finance. Supply chain finance offers convenient loan transactions that benefit all participants, including the buyer, supplier, and bank. However, poor credit risk management in supply chain finance may cause losses for finance providers and hamper the development of supply chain finance. Machine learning algorithms have significantly improved the accuracy of credit risk prediction systems in supply chain finance. However, their lack of interpretability or transparency makes decision-makers skeptical. Therefore, this study aims to improve AI transparency by ranking the importance of features influencing the decisions made by the system. This study identifies two effective algorithms, Random Forest and Gradient Boosting models, for credit risk detection. The factors that influenced the decision of the models to make them transparent are explicitly illustrated. This study also contributes to the literature on explainable credit risk detection for supply chain finance and provides practical implications for financial institutions to inform decision making.
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5 окт 2024
