Improve Budget Accuracy with a Custom Cash Flow Forecasting Model
AI algorithms analyze historical accounting data to identify complex patterns, improving forecast accuracy over spreadsheet models. They predict future inflows and outflows by learning from past payment cycles, seasonality, and customer behavior.
We built a forecasting system for a 25-person marketing agency that used QuickBooks. Their manual forecast had a 15% monthly variance. The new model, live in 3 weeks, reduced this variance to under 4% by identifying payment delays from their three largest clients.
The scope depends on connecting to accounting software like QuickBooks or Xero and the cleanliness of your data. A company with 24 months of consistent transaction data is a straightforward build. A business with mixed-source data from Stripe, payroll providers, and invoices requires more upfront data mapping.
What Problem Does This Solve?
Most finance teams start with their accounting software's built-in tools. QuickBooks Online offers a cash flow planner, but it projects based on simple past averages and cannot account for specific client payment terms. A reliable customer who always pays Net 60 consistently looks like a late payment to its model, throwing off the entire forecast.
Spreadsheets are the next step, but they require hours of manual data export and are notoriously prone to formula errors. A single broken cell reference can miss a large upcoming tax payment, causing an unexpected cash crunch. They cannot easily model multiple scenarios, like what happens if a key customer pays 15 days late.
Off-the-shelf forecasting tools like Float or Jirav connect to Xero or QBO but fail with complex business models. They struggle to ingest data from secondary sources like a custom CRM or a subscription platform like Chargebee. This means revenue from new product lines or non-standard contracts is often omitted, making the forecast incomplete.
How Does It Work?
We start by connecting directly to your QuickBooks Online or Xero API to pull 24-36 months of transaction-level history. Using Python and the httpx library for async requests, we also pull data from payment processors like Stripe and your CRM for sales pipeline context. We join these sources into a single time-series dataset, categorizing every invoice, bill, and payroll run.
We then build and test several forecasting models. A statistical baseline like ARIMA provides a starting point, but a gradient-boosted model built with XGBoost almost always performs better. It can incorporate over 50 features, including day of the week, individual customer payment history, and even deal stage data from the CRM. This allows the model to learn that invoices to 'Client A' are paid in 45 days on average, while 'Client B' pays in 22 days.
The final model is packaged into a FastAPI application and deployed on AWS Lambda. Once per day, a scheduled job pulls the latest data, retrains the model, and generates a new 90-day forecast. The forecast, including a 10% and 90% confidence interval, is written to a Supabase database and displayed on a simple web dashboard. The entire daily process completes in under 2 minutes.
We use structlog for structured logging, so if an API connection to QuickBooks fails, an immediate alert is triggered. The system sends a daily Slack notification with the updated 30-day cash position. The forecast data can also be pushed directly to a Google Sheet via its API for your finance team to use in existing reports. Total monthly cloud hosting costs are typically under $30.
What Are the Key Benefits?
See 90 Days Ahead, Updated Daily
Get a rolling 90-day cash flow forecast automatically updated every 24 hours. No more manual spreadsheet updates before your weekly finance meeting.
A Single Fixed Price for the Build
One fixed-price engagement to build and deploy the system. After launch, hosting is under $50/month with an optional flat-rate maintenance plan.
You Own the Forecasting Engine
We deliver the complete Python source code to your company's GitHub repository. There is no vendor lock-in and no per-user license fee.
Alerts When Data Sources Break
Built-in monitoring watches data connections to QuickBooks and Stripe. If a connection fails, you get an instant Slack alert instead of finding out from a bad forecast.
Connects To Your Accounting Tools
We pull data directly from QuickBooks Online, Xero, and Stripe. The final forecast can be pushed to a Google Sheet for your team's existing workflow.
What Does the Process Look Like?
Data Access and Audit (Week 1)
You provide read-only API access to your accounting and payment systems. We perform a data quality audit and deliver a report on data completeness.
Model Prototyping (Week 2)
We build and test several forecasting models on your historical data. You receive a performance summary showing backtested accuracy for each model.
Deployment and Dashboard Build (Week 3)
We deploy the final model on AWS Lambda and build a simple dashboard for viewing the forecast. You receive login credentials for initial testing.
Monitoring and Handoff (Week 4)
We monitor the daily forecast runs for one week and tune alerting. You receive the full source code, deployment runbook, and system documentation.
Frequently Asked Questions
- How much does a custom forecasting model cost and how long does it take?
- A typical build takes 3-4 weeks. The cost depends on the number of data sources and the cleanliness of your accounting data. Connecting only to QuickBooks with well-categorized transactions is straightforward. Integrating QBO, Stripe, and a custom CRM requires more complex data mapping. We provide a fixed-price quote after the initial discovery call.
- What happens if a forecast is wrong or an API fails?
- The model provides a confidence interval, showing a probable range for your cash position. If a data source API fails, the system sends an alert and will not generate a new forecast, reverting to the last good one. This prevents a single bad data pull from corrupting your forecast. We fix API issues within hours under our maintenance plan.
- How is this different from using a tool like Float?
- Float is excellent for high-level cash planning and manual scenario modeling based on historical averages. Our system builds a machine learning model that learns specific, non-obvious patterns from your raw transaction data, like how individual customer payment speeds affect cash flow. It is built for predictive accuracy, not just planning.
- Can the model handle one-off events like a large capital expenditure?
- Yes. The dashboard allows you to add manual adjustments for known future events that are not in your historical data, such as a major equipment purchase or a government grant. These are layered on top of the AI-generated forecast, so you can see their impact on your projected cash balance instantly.
- What kind of accuracy can we expect?
- For businesses with at least two years of clean data, we typically achieve a Mean Absolute Percentage Error (MAPE) of under 5% for a 30-day forecast. This compares to 10-20% MAPE for manual spreadsheet models. Accuracy depends heavily on the consistency of your historical data, which we assess in the first week.
- Do we need an engineer on staff to run this?
- No. The system is fully automated, including daily data pulls and model retraining. The runbook we provide covers common maintenance tasks. For any changes, like adding a new bank feed, our flat-rate monthly maintenance plan covers the engineering work, so you do not need a dedicated hire for a system that largely runs itself.
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