AI-Powered Precision: Forecasting Invoice Dilution in Supply Chain Finance

Unpacking Invoice Dilution: A Critical Challenge in Supply Chain Finance

      In the complex world of supply chain finance (SCF), "invoice dilution" or "payment dilution" represents a significant, often underestimated, risk. This term refers to the discrepancy between the originally approved invoice amount and the actual funds ultimately collected. For finance providers, this gap can erode profit margins, turning what seemed like a profitable transaction into a financial loss. While traditional invoice financing often deals with issues related to underlying deliverables, dilution in SCF typically stems from post-approval adjustments by buyers, such as debit entries, volume discounts, or counterclaims.

      Historically, managing dilution risk in receivables finance involved conservative advance rates or static reserves. In SCF, a common practice has been to rely on an Irrevocable Payment Undertaking (IPU) from the buyer, essentially a guarantee of full payment. However, this reliance can restrict SCF services primarily to investment-grade buyers, limiting market access and hindering broader adoption of these crucial financing mechanisms. The need for more dynamic, data-driven solutions is clear, as highlighted by a recent academic paper exploring advanced machine learning models for this very challenge (Koptev et al., 2026).

Evolving Beyond Traditional Risk Mitigation

      For decades, financial institutions have sought to refine their risk assessment models. The Interface Financial Group (IFG), a specialized supply chain finance provider, recognized the limitations of IPUs and pioneered deterministic algorithms. These data-driven approaches establish real-time dynamic credit limits for specific buyer-supplier pairs, factoring in historical dilution multipliers. While these heuristics have proven effective in mitigating "tail risk" – the risk of rare, extreme events – the pursuit of even greater precision led to exploring the integration of predictive Artificial Intelligence (AI) models.

      The goal was to move beyond simply identifying past patterns to proactively forecasting dilution for every proposed transaction. This shift from reactive management to predictive intelligence is crucial for enhancing overall accuracy and effectiveness, ensuring that finance providers can make more informed underwriting decisions and optimize their service offerings to a wider range of clients. Companies like ARSA Technology, which has been experienced since 2018 in developing robust AI solutions, understand the imperative for businesses to leverage such advanced analytics for superior financial performance.

The Power of Leakage-Free Two-Stage AI Models

      The academic paper introduces an innovative machine learning framework designed to augment existing deterministic algorithms for predicting invoice dilution. This framework, named ScoreAI, processes an extensive production dataset comprising millions of invoice-level transactions. A key innovation lies in its "leakage-free" design, where historical time-series statistics are meticulously computed using only invoices that strictly predate the current one. This prevents data leakage, a common pitfall in predictive modeling where information from the future "leaks" into the past, leading to over-optimistic (and unrealistic) performance estimates.

      The framework employs a sophisticated two-stage architecture for realistic deployment. The first stage acts as a crucial filter, identifying invoices that are likely to experience any dilution. The second stage then estimates the magnitude of that dilution for only the flagged invoices. This modular approach significantly improves interpretability and prevents a single model from having to master two distinct prediction tasks simultaneously. The integration of macroeconomic indicators like unemployment, GDP, and retail sales also adds vital broader context, enhancing the model's predictive power.

Stage 1: Detecting Dilution Events with XGBoost Classification

      The initial phase of the AI framework employs an XGBoost binary classifier. XGBoost, or Extreme Gradient Boosting, is a powerful and popular machine learning algorithm known for its efficiency and accuracy in classification tasks. In this context, its role is to predict whether a given invoice will experience any form of dilution (a "yes/no" decision). This classification step is critical for several operational benefits:

• Early Warning Systems: Identifying potential dilution early allows finance providers to take proactive measures.
• Triage: Prioritizing invoices that require additional review or scrutiny.
• Policy Adjustments: Enabling dynamic adjustments to reserves or credit limits based on real-time risk assessments.

      In a rigorous rolling-window evaluation, the XGBoost classifier demonstrated impressive stability and accuracy. It achieved a ROC-AUC (Receiver Operating Characteristic - Area Under Curve) between 0.9167 and 0.9222, with a mean of 0.9205. ROC-AUC measures a classifier's ability to distinguish between classes, with values closer to 1 indicating better performance. The average precision, another key metric for imbalanced datasets often found in fraud or risk detection, ranged from 0.8361 to 0.8498, reflecting the model's reliability in making relevant predictions. This means the model effectively flags invoices that are genuinely prone to dilution.

Stage 2: Quantifying Dilution Magnitude with Advanced Regression Models

      Once an invoice is flagged as likely to dilute by the first stage, the framework moves to the second stage: estimating the precise monetary magnitude of that dilution. For this, the researchers evaluated several regression model families, including:

• XGBoost: Again demonstrating its versatility, here used for regression.
• RandomForest: An ensemble learning method that builds multiple decision trees and merges them to get a more accurate and stable prediction.
• MLP (Multi-Layer Perceptron): A type of artificial neural network capable of learning complex non-linear relationships.
• FasterKAN (Kolmogorov-Arnold Networks): A newer type of neural network architecture designed to potentially offer improved interpretability and performance over traditional MLPs by using learnable activation functions on edges rather than nodes.

      To further enhance robustness and leverage the complementary strengths of these individual models, the research also explored ensemble variants (simple-average and weighted-average). An ensemble model combines predictions from multiple models to achieve better overall performance than any single model could on its own. The weighted ensemble achieved the best overall performance, with a mean RMSE (Root Mean Squared Error) of 1215.8 ± 99.7 and a WMAPE (Weighted Mean Absolute Percentage Error) of 16.82% ± 0.48 across seven rolling time-window holdout periods. These metrics indicate a strong ability to accurately predict the actual dilution amount, minimizing significant deviations from reality.

The Role of Macroeconomic Factors

      A notable aspect of the ScoreAI pipeline is the incorporation of macroeconomic indicators. While historical invoice data provides granular insights into buyer-supplier behavior, broader economic trends can significantly influence payment patterns and dilution risks. Indicators such as unemployment rates, Personal Consumption Expenditures (PCE), Gross Domestic Product (GDP), industrial production index, and retail sales offer a wider context for predictive models.

      An ablation study, where macroeconomic parameters were intentionally removed from the model, was conducted to quantify their incremental impact. The results of this study confirmed that these external economic signals contribute positively to the model's overall accuracy and stability. By providing a more holistic view that extends beyond internal transaction data, macroeconomic features allow the AI models to anticipate shifts in payment behavior that might not be evident from individual transaction histories alone.

Transforming Supply Chain Finance Operations

      The implications of this leakage-free, two-stage AI framework for supply chain finance providers are profound. By delivering highly accurate and stable predictions for invoice dilution, these models enable:

• Reduced Risk Exposure: Minimizing unanticipated losses from diluted payments.
• Optimized Capital Allocation: Adjusting advance rates and reserves with greater precision.
• Expanded Market Access: Offering SCF solutions to a broader range of buyers, including those previously deemed too risky due to reliance on IPUs.
• Operational Efficiency: Automating risk assessment and freeing up human analysts for more complex tasks.
• Enhanced Decision-Making: Providing forward-looking estimates that integrate seamlessly into real-time credit limit underwriting.

      Implementing such advanced analytics can give finance providers a significant competitive edge. For instance, AI Video Analytics solutions or AI Box Series from providers like ARSA Technology can be adapted to process and analyze diverse data streams, extending beyond video to incorporate financial data for similar predictive modeling and operational intelligence in other sectors.

Conclusion

      The advent of sophisticated machine learning models like the leakage-free two-stage architecture, integrating robust classifiers like XGBoost and advanced regressors including the novel Kolmogorov-Arnold Networks (KANs), marks a pivotal step in managing financial risk. By precisely predicting invoice dilution, supply chain finance providers can unlock new levels of efficiency, reduce financial exposure, and serve a wider market. This blend of technical depth and practical application ensures more secure and profitable financial ecosystems.

      Ready to enhance your financial operations with cutting-edge AI? Explore ARSA Technology's solutions and leverage AI for predictive intelligence tailored to your enterprise needs.

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      Source: Koptev, P., Kumar, V., Malkov, K., Shapiro, G., & Vikhanov, Y. (2026). Predicting Invoice Dilution in Supply Chain Finance with Leakage-Free Two-Stage XGBoost, KAN (Kolmogorov–Arnold Networks), and Ensemble Models. arXiv preprint arXiv:2602.15248.