Build a Custom Cash Flow Forecast Model That Learns From Your Data

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.

Syntora's approach to improving cash flow forecasting begins by understanding your operational context and data landscape. The first step involves a detailed discovery phase to identify all relevant financial data sources, including accounting platforms, payment processors, and bank feeds.

Following discovery, Syntora would design and implement the necessary data integrations. We connect directly to your financial data sources using their APIs, pulling 24-36 months of historical data from systems like QuickBooks Online, Stripe, and Xero. This raw transactional data, encompassing invoices, payments, and other relevant financial activities, would be staged in a secure data store, such as a Supabase database. We would then develop Python scripts, using libraries like httpx, to identify and address common data quality issues, ensuring the dataset is clean and consistent for analysis.

From this prepared dataset, our engineers would focus on feature engineering. We would create variables that capture the unique payment cycles and operational characteristics of your business, such as typical days to pay per client, invoice amount distribution, and recognized seasonality. We would then evaluate and test various statistical and machine learning models, including Gradient Boosting models built with the XGBoost library, to identify the most suitable approach for predicting invoice payment dates and amounts. Our focus is on models that accurately capture non-linear relationships within your financial data.

Once a model is selected and validated, it would be deployed as an automated service. A common architecture involves wrapping the trained model in a FastAPI service. A scheduled job would then regularly pull all open invoices from your accounting system, process them through the model, and generate an updated cash-inflow forecast for a defined period, typically 90 days. The output would be a clear data feed detailing expected cash receipts by day, week, and month.

For deployment, Syntora would containerize the application with Docker and deploy it to a cloud environment like AWS Lambda, configuring it to run on a nightly schedule. We would implement structured logging using libraries such as structlog to provide visibility into system operations. Monitoring capabilities would be established to track the system's performance and accuracy over time, with alerts configured to flag any significant deviations or data anomalies. This serverless architecture provides flexibility and scalability tailored to your business needs.