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

TL;DR

This paper presents a machine learning framework combining advanced models like Leakage Free Two Stage XGBoost, KAN, and ensemble methods to predict invoice dilution in supply chain finance, aiming to improve risk management and facilitate broader adoption.

Contribution

It introduces a novel AI-driven approach that enhances invoice dilution prediction accuracy beyond traditional deterministic methods using extensive real-world data.

Findings

Improved prediction accuracy over existing methods

Effective use of advanced ensemble and network models

Potential to reduce reliance on buyer's irrevocable payment undertakings

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.