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Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models

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  • Pavel Koptev
  • Vishnu Kumar
  • Konstantin Malkov
  • George Shapiro
  • Yury Vikhanov

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.

Suggested Citation

  • Pavel Koptev & Vishnu Kumar & Konstantin Malkov & George Shapiro & Yury Vikhanov, 2026. "Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models," Papers 2602.15248, arXiv.org.
  • Handle: RePEc:arx:papers:2602.15248
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    References listed on IDEAS

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    1. Bakker, Marie-Renee & Klapper, Leora & Udell, Gregory F., 2004. "Financing small and medium-size enterprises with factoring: global growth and its potential in eastern Europe," Policy Research Working Paper Series 3342, The World Bank.
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