Build a Custom Cash Flow Forecast Model That Learns From Your Data
Predictive accounting algorithms analyze historical invoices to identify payment patterns that manual spreadsheets miss. They project future cash positions with under 5% error, replacing guesswork with a data-driven forecast.
We built a forecasting engine for a 25-person creative agency with inconsistent project-based revenue. They had three years of QuickBooks data but their manual forecast was consistently off by over 20%. The new system went live in four weeks, reducing their forecast variance to under 3% and giving them 60 days of runway visibility.
The system's complexity depends on your data sources. A business with 24 months of clean data in QuickBooks Online is a straightforward build. A company pulling from Stripe, a custom ERP, and bank statements requires more data mapping and cleaning before a model can be trained.
What Problem Does This Solve?
Most growing businesses start with Excel or Google Sheets for cash flow forecasting. This approach is fragile. A single formula error can corrupt the entire forecast, and the process is slow, often taking a finance person a full day each month to pull data from multiple systems and update the sheet.
Off-the-shelf tools in QuickBooks or Xero are a step up, but they have a critical flaw: they forecast based on invoice due dates, not actual customer payment behavior. If a major client consistently pays 15 days late, these tools still project their payments arriving on time, creating a permanent gap between the forecast and reality.
Dedicated forecasting platforms like Float or Jirav offer more power but are often built for SaaS companies with predictable subscription revenue. A 30-person logistics company with hundreds of clients on different payment terms finds these tools are a poor fit. They are forced into manual overrides and complex configurations that defeat the purpose of automation, all while paying a high monthly subscription.
How Does It Work?
First, we connect directly to your financial data sources using their APIs. We pull 24-36 months of history from systems like QuickBooks Online, Stripe, and Xero. This raw data, including every invoice and its corresponding payment, is staged in a Supabase database. An initial Python script using the httpx library identifies and flags common issues, like over 400 invoices with mismatched payment records in a recent project.
From this clean dataset, we engineer features that capture your business's unique payment cycles. We create variables like 'days to pay per client', 'invoice amount decile', and seasonality flags. We then test multiple models. A Gradient Boosting model, built with the XGBoost library, consistently outperforms simpler time-series models by capturing non-linear relationships. Our final model for a recent client used 75 features to predict invoice payment dates with a mean absolute error of just 2.1 days.
This trained model is wrapped in a FastAPI service. Each night, a scheduled job pulls all open invoices from your accounting system, runs them through the model, and generates a new cash-inflow forecast for the next 90 days. The entire process for a company with 1,500 open invoices completes in under 30 seconds. The output is a simple data feed showing expected cash receipts per day, week, and month.
We containerize the application with Docker and deploy it on AWS Lambda, where it runs on a nightly schedule. We use structlog for structured logging, and if forecast accuracy drops below a 5% error threshold for two consecutive weeks, a custom alert is sent to Slack. This serverless architecture ensures the system runs reliably with hosting costs typically under $25/month.
What Are the Key Benefits?
A 90-Day Forecast in 30 Seconds
The system runs automatically every night. Your team gets an updated forecast report by 8 AM without any manual data entry or spreadsheet work.
Pay Once, Own Forever
This is a fixed-price build with no recurring license fees. You avoid the $500+/month subscription costs of enterprise forecasting tools.
You Get the Source Code
You receive the complete Python source code in your GitHub, a Dockerfile for deployment, and a system runbook. There is no vendor lock-in.
Alerts Before a Cash Crunch
The model re-evaluates risk daily. If a large client's payment pattern suddenly changes, you get an alert, not a surprise deficit next month.
Connects to Your Real Books
Direct API integration with QuickBooks, Xero, and Stripe means the forecast is always based on live data, not stale CSV exports.
What Does the Process Look Like?
System & Data Access (Week 1)
You provide read-only API keys for your accounting platform and payment systems. We perform a data quality audit and deliver an initial findings report.
Model Prototyping (Week 2)
We build and test predictive models. You receive a performance summary explaining which factors best predict payment timing for your business.
API Build & Deployment (Week 3)
We build the FastAPI service and deploy it to your cloud infrastructure. You receive access to a staging environment to validate the forecast output.
Monitoring & Handoff (Week 4+)
We monitor the live model's accuracy for 30 days post-launch. You receive the full source code repository and a system runbook detailing maintenance.
Frequently Asked Questions
- How much does a custom forecasting system cost?
- Pricing is scoped based on the number and complexity of your data sources. A business using only QuickBooks Online is straightforward. One pulling from a custom ERP and multiple payment processors requires more integration work. We provide a fixed-price quote after a 30-minute discovery call where we map out your specific systems and data needs.
- What happens if the forecast is wrong?
- The system tracks its own accuracy by comparing predictions to actual payments. If the Mean Absolute Percentage Error (MAPE) exceeds 5% for a week, an automated alert is triggered. This usually means a shift in client behavior, signaling the need for a model retrain. This is covered under our flat monthly maintenance plan.
- How is this different from a tool like Float?
- Float is a visual tool for manual, 'what-if' scenario planning based on invoice due dates. Our system is a predictive engine. It learns the actual payment behavior of your clients to forecast when cash will really arrive, not just when it is due. It is built for probabilistic accuracy over manual scenario modeling.
- Do we need an engineer on staff to run this?
- No. The system runs automatically on serverless infrastructure like AWS Lambda, which requires no server management. The handoff includes a runbook that documents common operational tasks. We also offer a flat monthly maintenance plan to handle all monitoring, retraining, and any required updates for you.
- Our data is messy. Can you still build a model?
- Yes, data cleaning is a standard part of our process. We need at least 12 months of transaction history with a minimum of 500 paid invoices to build a reliable model. Our initial data audit identifies any major quality issues. If the data is insufficient, we will recommend waiting to start the build.
- Does the system also forecast expenses?
- This build focuses on cash inflow from accounts receivable, which is the most volatile part of cash flow. We can build a second model for accounts payable, but we scope it as a separate project. Predicting expenses is often simpler as many are fixed. We solve the biggest uncertainty first, which is revenue collection.
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