無洩漏兩階段 XGBoost、KAN 預測供應鏈發票稀釋

• Two-stage XGBoost architecture designed to prevent data leakage in temporal financial predictions • Kolmogorov-Arnold Networks (KAN) integration for capturing non-linear relationships in invoice payment behavior • Ensemble modeling approach combining multiple algorithms to improve prediction robustness and generalization • Training conducted on production datasets spanning nine key transaction fields (specific fields not detailed in available sources) • Real-time dynamic credit limit generation enabling per-buyer-supplier pair risk assessment • Framework supplements rather than replaces deterministic algorithms, suggesting hybrid approach to risk management

The convergence of machine learning frameworks with supply chain finance suggests a structural shift away from static risk management tools (IPUs) toward dynamic, data-driven credit assessment. This transition could democratize supply chain financing access for sub-investment grade buyers while reducing margin losses for financial institutions. Broader adoption of AI in procurement and logistics—demonstrated by 20-30% improvements in demand forecasting and up to 40% reductions in supply chain disruptions—indicates that predictive analytics will become foundational to supply chain operations. However, the expansion of AI across supply chain ecosystems creates new vulnerabilities, including potential data poisoning attacks on training datasets and supply chain compromises affecting AI-enabled systems[7]. Organizations will need to balance efficiency gains against emerging cybersecurity risks in AI-driven financial and logistics infrastructure.