Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models
arXiv:2602.15248v1 Announce Type: new 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.
arXiv:2602.15248v1 Announce Type: new 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.
Executive Summary
This article proposes an innovative AI and machine learning framework to predict invoice dilution in supply chain finance using a leakage-free two-stage XGBoost model, KAN, and ensemble models. By leveraging real-time dynamic credit limits and production datasets, the authors aim to mitigate non-credit risk and margin loss in supply chain finance. The study evaluates the effectiveness of this framework in predicting dilution for each buyer-supplier pair, thereby providing valuable insights for supply chain finance adoption. The results demonstrate the potential of data-driven methods in supplementing traditional deterministic algorithms, which can hinder supply chain finance adoption among sub-invested grade buyers. Overall, the study contributes to the growing field of supply chain finance and highlights the importance of AI and machine learning in risk management.
Key Points
- ▸ Introduction of an AI and machine learning framework to predict invoice dilution
- ▸ Use of leakage-free two-stage XGBoost model, KAN, and ensemble models
- ▸ Evaluation of the framework's effectiveness using production datasets and real-time dynamic credit limits
Merits
Strength in Predictive Accuracy
The study demonstrates the high predictive accuracy of the proposed framework in predicting invoice dilution, outperforming traditional deterministic algorithms.
Adoption of Data-Driven Methods
The article highlights the potential of data-driven methods in supplementing traditional algorithms, enabling more efficient risk management in supply chain finance.
Demerits
Limited Generalizability
The study's results may not be generalizable to other industries or contexts due to the specific nature of the production datasets used.
Dependence on High-Quality Data
The effectiveness of the proposed framework relies heavily on the quality and availability of high-quality data, which may not always be feasible in practice.
Expert Commentary
This article represents a significant contribution to the field of supply chain finance, as it introduces an innovative AI and machine learning framework for predicting invoice dilution. The study's results demonstrate the high predictive accuracy of the proposed framework, which has the potential to mitigate non-credit risk and margin loss in supply chain finance. However, the study's limitations, including the limited generalizability of the results and the dependence on high-quality data, should be carefully considered. Overall, the article highlights the importance of data-driven methods in risk management and provides valuable insights for supply chain finance providers and policymakers.
Recommendations
- ✓ Future studies should investigate the application of the proposed framework in other industries and contexts to assess its generalizability.
- ✓ Supply chain finance providers should consider adopting data-driven methods, such as the proposed framework, to improve risk management and reduce margin loss.