The term "AI-driven cash forecasting" has become a buzzword among Treasury Management System (TMS) providers. Many claim to leverage artificial intelligence (AI) and machine learning (ML) to enhance their forecasting capabilities. However, a deeper analysis reveals that these claims often lack substance, particularly when it comes to accurate sales and cash inflow predictions.
While TMS providers may use historical data and statistical modeling to estimate predictable outflows such as payroll, vendor payments, and recurring expenses, their ability to model receivables (and by extension, sales) using a "one-size-fits-all" AI approach is highly questionable. Without proper backtesting, model calibration, and weighting of macro and microeconomic factors, these AI forecasts risk being nothing more than glorified spreadsheets dressed up as intelligent systems.
This article will explore why TMS providers' AI claims are misleading, and what true AI-driven cash forecasting should look like.
Unlike outflows, which can often be reasonably estimated based on contract terms, past payment behavior, and fixed schedules, cash inflows—specifically from sales and receivables—are much harder to predict.
Cash inflows are not just a function of past trends, but are influenced by:
1. Macroeconomic Conditions: Inflation, interest rates, economic downturns, and government regulations.
2. Industry-Specific Micro Factors: Customer behavior, seasonal demand fluctuations, regulatory approval cycles, and competitive landscape.
3. Business-Specific Internal Factors: Sales pipeline strength, marketing efforts, operational bottlenecks, and product pricing.
TMS providers rarely integrate these factors into their forecasting models in a meaningful way. Instead, they rely on basic historical data extrapolation, which does not constitute true AI.
Let's examine three distinct industries—marijuana businesses, medical device companies, and infrastructure projects—to highlight how different macro and microeconomic factors shape cash inflows.
- Macroeconomic factors impacting sales: Legislation changes, taxation, consumer sentiment, black market competition.
- Micro factors impacting receivables: Cash-based transactions, banking restrictions, seasonality (e.g., 4/20 sales spikes).
- AI Challenge: Sales are heavily regulation-driven and cannot be predicted with simple historical models.
TMS Misrepresentation Example: A TMS claiming "AI-driven cannabis cash forecasting" would need to integrate legal updates, taxation shifts, and consumer demand modeling, rather than just extrapolating past sales trends.
- Macroeconomic factors impacting sales: FDA approvals, insurance reimbursement policies, hospital procurement cycles.
- Micro factors impacting receivables: Sales rep performance, distributor agreements, warranty and service contracts.
- AI Challenge: AI must factor in regulatory timelines, hospital budgets, and insurance payouts, which vary significantly across geographies and product types.
TMS Misrepresentation Example: A TMS claiming "AI-driven sales forecasting for med-tech" must account for delays in hospital purchasing decisions and changing insurance reimbursement policies—something few systems do.
- Macroeconomic factors impacting sales: Government infrastructure budgets, interest rates, raw material price fluctuations.
- Micro factors impacting receivables: Contract milestones, payment retention clauses, labor shortages, weather disruptions.
- AI Challenge: A project-based business does not follow linear revenue patterns. Payments are tied to milestones and can be delayed by external factors.
TMS Misrepresentation Example: A TMS claiming "AI-powered forecasting for construction cash flows" must be able to simulate project delays and cost overruns, rather than simply applying a linear growth model.
For any AI system to be credible in forecasting future cash flows, it must:
- Backtest its models using real historical data.
- Analyze correlation and causation of macro & microeconomic factors.
- Continuously adjust factor weightages based on real-world results.
- Incorporate human feedback via Reinforcement Learning (RLHF).
Most TMS providers fail this credibility test because they:
❌ Do not conduct backtesting against external economic data.
❌ Rely only on historical cash flow trends without external validation.
❌ Do not allow weightage customization based on industry-specific insights.
❌ Ignore the need for human-in-the-loop AI to refine predictions.
Without these elements, any AI claim is just a marketing gimmick.
To be truly AI-driven, a cash forecasting solution must:
- Use macroeconomic indicators (interest rates, inflation, regulatory shifts) to dynamically adjust forecasts.
- Customize industry-specific models (marijuana, medical devices, infrastructure).
- Backtest against historical external events (e.g., COVID-19 impact on sales).
- Refine predictions with user feedback (Reinforcement Learning).
- Be explainable—finance teams should understand how AI arrived at a given forecast.
Any system lacking these components should not claim to be "AI-powered."
TMS providers are rushing to brand their solutions as "AI-driven," but most fail to implement the key elements of predictive intelligence.
Before trusting an AI cash forecasting claim, ask:
- Does it incorporate macro & microeconomic factors?
- Has it been backtested against real-world financial data?
- Does it adjust weightages dynamically instead of assuming static trends?
- Can users influence the AI with feedback mechanisms?
Until these questions are satisfactorily answered, many so-called "AI-powered" TMS cash forecasts are nothing more than statistical extrapolation in disguise.
It's time for businesses to demand real AI—not just marketing hype—from their Treasury Management System providers.
