Supply Chain Finance Without IPU
Written by: Sabeen Ahmed, Chief Credit Officer, The Interface Financial Group
How Real-Time Data-Driven Dynamic Credit Limits and Predictive AI Are Changing Dilution Risk Management
For decades, the growth of Supply Chain Finance (SCF) has rested on a single legal instrument: the irrevocable payment undertaking (IPU). When a buyer approves a supplier's invoice and commits unconditionally to pay it in full, the finance provider's risk collapses to little more than the buyer's credit risk. That makes early payment cheap and simple to underwrite — which is why programs at companies like Siemens, Walmart, and Michelin are built on it.
But the IPU is also the industry's bottleneck. IPU-based programs are effectively limited to investment-grade — fewer than 8,000 companies globally and near-investment-garde buyers, against more than 300 million businesses worldwide (Shapiro, Babich, & Wuttke, 2026). Even eligible buyers often decline to sign: an irrevocable commitment strips away their ability to use legitimate chargebacks, setoffs, withhold and counterclaims against the payments to suppliers. The result is that a huge market of sub-investment grade buyers and their suppliers unable to use SCF early payment programs.
Two recent studies — an empirical analysis published in the International Journal of Production Economics (Shapiro et al., 2026) and a machine learning white paper from AI1 Technologies and The Interface Financial Group (Koptev, Kumar, Malkov, Shapiro, & Vikhanov, 2026) — suggest the industry may be entering what the first paper calls a “post-irrevocable payment undertaking era.” Together they describe how dilution risk can be measured, priced, and predicted rather than contractually eliminated. For practitioners in SCF, factoring, and ABL lending, the findings deserve attention.
The problem: dilution
In SCF, dilution is the gap between the approved invoice amount and what the buyer actually pays. It arises from setoffs, chargebacks, volume discounts, counterclaims, and other post-approval deductions. The IPU exists precisely to eliminate this risk for the funder.
Here is the number that should reframe how practitioners think about it: in the transaction data studied, 99.8% of suppliers experienced at least one dilution over a five-year rolling horizon. Compare that with the credit risk — in 2024, no S&P-rated AAA, AA, or A firm defaulted, and only 0.05% of BBB-rated firms did (Shapiro et al., 2026). Dilution isn't a tail risk, it's the base case. It is the routine, dominant source of non-credit loss in receivables-based finance, and traditional tools — corporate credit scores, flat advance rates — are poor predictors of it.
The alternative: Dynamic Credit Limits
The Dynamic Credit Limit (DCL) approach, implemented at scale by The Interface Financial Group, replaces the buyer's guarantee with a real-time, data-driven credit limit for each buyer–supplier pair. This method makes SCF available to the larger sub-investment-grade buyer market. Instead of asking “will this buyer promise to pay in full?”, the algorithm asks “based on everything we know about this relationship, how much of this invoice portfolio is safe to advance right now?”
The limit is computed from three families of signals: the buyer–supplier pair's historical dilution behavior (how often invoices are reduced after approval, and by how much), payment-level deductions not tied to specific invoices, and the supplier's billing regularity and overall risk score, including forward-looking viability. A buyer-specific ceiling, recalibrated monthly, caps exposure to riskier buyers.
Protection comes from a layered automatic recourse structure rather than a guarantee. First, the provider holds back a reserve — advancing, say, $900,000 against a $1 million of approved portfolio. Second, if predicted dilution exhausts that reserve, the shortfall is automatically recovered from future payments flowing from the same buyer to the same supplier — a low-cost, fully digital mechanism that also incentivizes both parties to keep submitting and approving invoices. Only third, and rarely, does the provider look to the supplier's other receivables or escalate to collection, the expensive path.
Operationally, this demands a digitally integrated platform: API or MCP connections to thousands of real-time data sources and enough computing performance to recalculate limits within 15–25 milliseconds (Shapiro et al., 2026). The method of using recourse is not new, but the digital underwriting system, with the dilution prediction and automatic real-time dynamic credit limit for each buyer-supplier dyad (account debtor - client) at specific moments of time is the innovation.
Does it work? The evidence
The IJPE study analyzed 32,536 DCL transactions with over $5 billion in volume spanning ten years, covering large number of suppliers and 24 buyers across the United States, Canada, the United Kingdom, Europe, Australia, and parts of Asia. The authors compared DCL against a counterfactual of traditional invoice financing at various flat advance rates as well as credit-score-based advance rates.
The headline results are notable on both sides of the risk-return trade-off. On funding: suppliers under DCL received advances averaging over 90% of approved receivables — roughly half again more liquidity than the typical flat invoice-financing effective advance, it takes into account all eligible and ineligible AR. On risk: when the comparison was matched for funding level, invoice financing would have breached the first recourse buffer 90% more often than DCL. Escalations to collection told the same story: only 0.26% of transactions exceeded the second recourse basis under DCL, versus 1.38% under the invoice-financing counterfactual — a statistically and economically significant difference (Shapiro et al., 2026).
In efficient-frontier terms, DCL dominated the alternatives: more funding at the same risk, less risk at the same funding. That is a rare combination, and it holds regardless of the funder's risk appetite. Average dilution across the funded portfolio ran at 3.8% of invoices funded — real money but absorbed almost entirely within the first two automatic recourse layers.
The next layer: predicting dilution with AI engine
The deterministic DCL algorithm looks backward — it extrapolates from historical dilution patterns. The natural question is whether current predictive AI can add a forward-looking estimate for each proposed transaction. That is the subject of the second study (Koptev et al., 2026), built on IFG production data comprising roughly 4.8 million invoice records.
The ScoreAI framework uses a two-stage architecture that mirrors how risk teams actually work. Stage 1 is a classifier that answers a triage question: is this invoice likely to dilute at all? Stage 2, invoked only when risk is flagged, estimates how much. A critical design discipline runs through the pipeline: all historical features are “leakage-free,” computed strictly from invoices dated before the one being scored — so reported performance reflects what the model could genuinely know at decision time, not hindsight.
The results are strong enough to be operationally useful. The Stage 1 classifier achieved ROC-AUC between 0.9167 and 0.9222 across seven rolling four-year evaluation windows — stable discrimination, not a lucky window. For magnitude estimation, a weighted ensemble of four model families (XGBoost, RandomForest, MLP, and FasterKAN) outperformed every individual model, with a weighted mean absolute percentage error of about 16.8%. Interestingly, an ablation test showed macroeconomic indicators (GDP, unemployment, retail sales, and similar) added less than 1% improvement — the predictive power lives overwhelmingly in transaction-level behavioral history, not the macro backdrop (Koptev et al., 2026).
The practical implication: a funder can use the classifier for early warning and triage — flagging invoices for review, adjusting reserves, or tightening limits — while the magnitude model feeds an expected-dilution estimate into the credit limit itself. The authors are candid about limitations: errors grow in the tails, so extreme dilution events likely need dedicated handling such as quantile estimates or policy caps.
What practitioners should take away
Three lessons stand out. First, dilution is measurable and predictable at scale. The near-universal incidence of dilution, combined with 0.92 ROC-AUC predictability, means the industry's historical reliance on buyer guarantees and blunt flat reserves reflects a data gap, not a fundamental necessity. Second, the guarantee is not the only path to safety. A layered recourse structure driven by real-time behavioral data delivered both more funding and fewer losses than conventional approach in a decade of live transactions. Third, the winners are platforms, not paperwork. Everything above depends on digital integration — real-time invoice, payment, cash movement and adjustment data flowing continuously and constantly into the underwriting engine.
None of this makes the IPU obsolete where it is available. But for the enormous market of sub-investment-grade buyers and their underfinanced suppliers, these studies offer evidence that supply chain finance can be extended safely without it. For funders, that is not just a risk-management story — it is a growth story!
About the Author
Sabeen Ahmed is Chief Credit Officer at The Interface Financial Group, a global digital supply chain finance firm. Sabeen oversees the company's credit and risk framework and works closely with the product team on the analytics behind its credit decision engine. A proud Canadian who grew up in Montreal, she began her career in underwriting across Canada's Big 5 banks, an experience that gave her a ground-level view of credit and still shapes how she leads today. An avid sci-fi junkie with a Chartered Management Accounting degree from McGill University and a Master's in Finance from George Washington University, she aims to boldly go where no one has gone before.
References
Shapiro, G., Babich, V., & Wuttke, D. (2026). Towards a post-irrevocable payment undertaking era: Dynamic credit limits in supply chain finance. International Journal of Production Economics, 295, Article 109963. https://doi.org/10.1016/j.ijpe.2026.109963
Koptev, P., Kumar, V., Malkov, K., Shapiro, G., & Vikhanov, Y. (2026). Predicting invoice dilution in supply chain finance with leakage-free two-stage models: XGBoost, KAN (Kolmogorov–Arnold Networks), and ensembles [White paper]. AI1 Technologies & The Interface Financial Group. https:// https://arxiv.org/abs/2602.15248
The views expressed in the Commercial Factor website are those of the authors and do not necessarily represent the views of, and should not be attributed to, the International Factoring Association.