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[2602.15248] Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models
Summary
This paper presents a machine learning framework to predict invoice dilution in supply chain finance, utilizing advanced models like XGBoost and Kolmogorov Arnold Networks.
Why It Matters
Invoice dilution poses significant financial risks in supply chain finance, particularly for lower-rated buyers. This research introduces innovative predictive methods that could enhance risk management and facilitate broader adoption of supply chain finance solutions.
Key Takeaways
- Introduces a machine learning framework for predicting invoice dilution.
- Utilizes advanced models like XGBoost and Kolmogorov Arnold Networks.
- Addresses financial risks associated with invoice dilution in supply chain finance.
- Proposes real-time dynamic credit limits to improve risk management.
- Evaluates the framework using an extensive dataset across key transaction fields.
Computer Science > Artificial Intelligence arXiv:2602.15248 (cs) [Submitted on 16 Feb 2026] Title:Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models Authors:Pavel Koptev, Vishnu Kumar, Konstantin Malkov, George Shapiro, Yury Vikhanov View a PDF of the paper titled Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models, by Pavel Koptev and 4 other authors View PDF Abstract: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. Subjects: Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Mathematical Finance (q-fin.MF) Cite as: arXiv:2602.15248 [cs.AI] (or arXiv:2602....