CAPIX Treasury Software

🧠 **Which AI model is actually best for cash flow forecasting?**

Short answer: no single one. We put the leading Large Quantitative Models head-to-head — LSTM, Temporal Fusion Transformers, Prophet, DeepAR, XGBoost, and the new wave of time-series foundation models like TimeGPT and Chronos — to see which is best suited to corporate treasury forecasting and risk management.

Here's what we found:

📈 **Temporal Fusion Transformer** — the strongest single model for multi-horizon, multi-currency forecasting
🌳 **XGBoost** — fast and explainable for structured, tabular drivers
📅 **Prophet / NeuralProphet** — great interpretable baseline for seasonal cash flows
📊 **GARCH & probabilistic models** — the right tools for volatility and risk bands, not point forecasts
🤖 **LLMs (ChatGPT/DeepSeek/Grok-class)** — not forecasters, but a powerful reasoning layer for explaining forecasts and driving our autonomous agents

The real answer isn't "pick one" — it's a **layered hybrid**, and that's exactly what powers CAPIX's forecasting engine today.

👉 Read the full comparison: https://capix.net/ai/large-quantitative-models/

CAPIX Treasury Software CAPIX provides Microsoft-based treasury software designed for integration with Dynamics 365, SAP and other leading ERP platforms.

🚀 We're bringing our AI in-house.

For years, CAPIX has run its AI cashflow forecasting and treasury models in the cloud. Now we're moving the heavy lifting onto our own hardware — and we
couldn't be more excited about it. 🧠💻

At the centre of our strategy: the NVIDIA DGX Spark — a compact AI supercomputer that puts data-centre-class power right on the desk.

Why the shift to local hardware?

🔒 Data sovereignty — treasury data is among the most sensitive a business holds. Now it never has to leave our controlled environment.
💷 Predictable costs — owning the hardware turns an unpredictable cloud bill into a known, fixed cost.
⚡ Speed & efficiency — keeping models and data together means faster forecasting and tighter feedback loops.
🛠️ Independence — we control the full stack, from the silicon to the treasury app.

It's not goodbye to the cloud — it's putting each workload where it runs best. The result for our customers: AI-driven treasury tools that are faster, more
cost-efficient, and private by design. ✅

👉 Read the full story: https://capix.net/ai/local-hardware/

Local AI Hardware for Treasury CAPIX is moving large quantitative AI models from the cloud to local hardware for greater control, data sovereignty and efficiency — with the NVIDIA DGX Spark at the centre of our strategy.

Beyond the Forecast: How Agentic AI is Closing the Gap from Insight to Action in Treasury

For years, the promise of artificial intelligence in treasury has been largely about prediction—using sophisticated models to generate more accurate cash flow forecasts. While this has been a monumental step forward, it only solves half the problem. A forecast, no matter how precise, is still just a piece of information. Acting on it—reconciling discrepancies, communicating with counterparties, and adjusting positions—has remained a manual, time-consuming drag on finance teams.

We believe the future of treasury isn't just smart forecasting; it's autonomous action. By integrating **Agentic AI** with powerful large language models (LLMs), we are moving from passive prediction to active liquidity management. This new paradigm creates a self-orchestrating system that doesn't just tell you what will happen—it helps make it happen.

The Rise of the Treasury Agent Mesh

The concept of agentic AI refers to systems where AI agents don't just generate text or numbers, but pursue goals and execute tasks autonomously . Imagine a mesh of specialized digital teammates, each with a specific expertise, working in concert under the supervision of a treasury professional .

For CAPIX, this translates into a seamless workflow that bridges the gap between your ERP data and real-world financial actions. Here is how the architecture comes to life:

1. The "Collector" Agents: Intelligent Data Ingestion
The foundation of any forecast is data, but treasury data is notoriously siloed across ERPs (like SAP, Oracle, and Dynamics 365), bank portals, and spreadsheets . "Collector" agents, powered by models like **Anthropic's Opus** for complex reasoning, can autonomously navigate these disparate systems. They don't just pull raw data via APIs; they understand context. They can interpret a partial invoice match, identify a missing payment reference, and even flag anomalous data that could skew a forecast . They prepare a clean, harmonized dataset ready for analysis.

2. The "Analyst" Agents: Next-Generation Forecasting
With clean data in hand, the system leverages CAPIX's existing hybrid AI models—combining LSTM networks, Transformers, and XGBoost—to generate the core forecast . But this is where the agentic layer adds a new dimension. An "Analyst" agent can take this output and run dynamic "what-if" scenarios based on real-time market cues from a Market Monitoring agent . If the market shifts, the Analyst agent immediately recalibrates the forecast, providing not just a static number, but a continuously evolving, risk-adjusted view of liquidity .

3. The "Executor" Agents: Closing the Loop on Action
This is the true game-changer. Once a forecast identifies a potential shortfall or surplus, the "Executor" agents spring into action.
- **Prescriptive Workflows:** If a liquidity gap is detected, an agent can autonomously verify the cause, run simulations (e.g., "What if we delay this supplier payment by three days?"), and present a recommended course of action .
- **Automated Communication:** Upon approval (or within pre-set rules), an agent can draft and send payment instructions to counterparties or funding instructions to banks via secure APIs .
- **Internal Coordination:** The agent can also notify management via tools like Microsoft Teams, providing a clear, auditable summary of the action taken and the reasoning behind it . It might even prompt a procurement team to confirm a goods receipt to clear a payment bottleneck .

The Strategic Shift: From Operator to Overseer

This agentic workflow transforms the treasury department. Your team is no longer spending hours gathering data and chasing exceptions. Instead, they become strategic overseers of an intelligent system. They interact with a natural language interface—a "Treasury AI Assistant"—asking questions like, *"What's our forecasted position next month under a stressed scenario?"* or *"Optimize our intercompany loans to reduce FX exposure"* .

The agents handle the underlying complexity, providing full transparency and a tamper-proof audit trail for every action taken .

Using Meta's Prophet for Cash Flow Forecasting

Financial forecasting has always been more art than science, but new tools are shifting the balance. One unexpected contender is Prophet, an open-source forecasting library created by Meta’s data science team. Originally built for internal metrics, it’s now being adopted by finance teams to model cash flows with greater accuracy and less manual effort.

What Makes It Useful

Prophet is designed to handle real-world time series data—exactly what cash flow forecasting requires. It automatically accounts for trends, weekly/monthly/quarterly cycles, holiday impacts, and outliers. This means finance teams can spend less time adjusting Excel formulas and more time analyzing outputs.

For example:

A retail business can model daily cash intake, adjusting automatically for seasonal peaks.

A SaaS company can project recurring revenue while factoring in churn cycles.

A manufacturer can forecast payment cycles against operational expenditures.

Practical, Not Perfect

Prophet isn't magic. It requires clean historical data and thoughtful parameter tuning. But its strength lies in its flexibility and interpretability. Analysts can incorporate known future events—like a new product launch or contract expiration—directly into the model, blending quantitative forecasts with qualitative insight.

Why It’s Gaining Traction

Open source and accessible – works in Python/R, with a relatively gentle learning curve for analysts familiar with time series.

Produces probabilistic forecasts – outputs a range of possible outcomes, not just a single line, helping teams assess risk.

Handles missing data and outliers gracefully – real-world cash flow data is rarely perfect, and Prophet is built for that reality.

In Practice

Teams using Prophet often pair it with existing ERP or financial systems, using it for scenario planning and medium-term forecasts rather than replacing core accounting tools. The goal isn’t absolute precision, but better-informed decision-making—understanding the likely range of future cash positions so businesses can plan with more confidence.

If you're exploring forecasting tools, Prophet is worth a look—especially if your current process involves heavy manual adjustment for seasonality or irregularities. It won’t replace financial judgment, but it can make the quantitative foundation stronger.