Paper: arXiv 2602.15248

Authors: 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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on straightforward machine learning implementations (XGBoost, FasterKAN, Ensembles) without deep mathematical derivations, but demonstrates exceptional empirical rigor through standard out-of-time evaluation across 4.8 million transaction records. The two-stage, leakage-free design for predicting supply chain invoice dilution provides practical commercial value and solid ablation testing.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict Invoice Dilution & Enable Dynamic Credit Limits in SCF] --> B[Data Input: 9 Production Transaction Fields]
    B --> C[Data Preprocessing & Leakage-Free Temporal Split]
    C --> D[Model Training: Two-Stage XGBoost, KAN & Ensembles]
    D --> E[Evaluation: Deterministic Baseline vs. ML Models]
    E --> F[Outcome: ML Ensembles Outperform Baseline, Reducing Risk & Margin Loss]