用机器学习预测供应链金融中发票收款缩水，提升资金安全。
Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models
- 分两阶段结合XGBoost、KAN与集成模型预测付款差异
- 基于九个关键字段的生产数据实现实时风险预判
- 适合关注供应链金融风控的金融机构和平台方
发票稀释（即批准金额与实际收款之间的差距）是供应链金融中非信用风险和利润损失的重要来源。传统上依赖买方不可撤销付款承诺（IPU）来管理该风险，但IPU可能阻碍中小买方参与供应链金融。本文提出一种基于数据驱动的AI机器学习框架，利用涵盖九个关键交易字段的生产级数据集，实时预测每个买卖双方的发票稀释情况，评估其对确定性算法的补充效果，为动态信用限额提供支持。
原文摘要 · Abstract (English)
Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source of non credit risk and margin loss in supply chain finance. Traditionally, this risk is managed through the buyer's irrevocable payment undertaking (IPU), which commits to full payment without deductions. However, IPUs can hinder supply chain finance adoption, particularly among sub-invested grade buyers. A newer, data-driven methods use real-time dynamic credit limits, projecting dilution for each buyer-supplier pair in real-time. This paper introduces an AI, machine learning framework and evaluates how that can supplement a deterministic algorithm to predict invoice dilution using extensive production dataset across nine key transaction fields.
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