Every successful AI project starts with clarity of purpose. For the Cashflow Forecasting MVP, the goal was simple but powerful: enable finance leaders to see 13 weeks ahead with confidence. We began by defining measurable outcomes—forecast accuracy, working-capital visibility, and CFO trust. Using the CPMAI framework, this phase aligned business pain points with AI potential, translating liquidity challenges into a data-driven opportunity. The MVP will demonstrate how structured problem framing and agile iteration can transform routine cash projections into proactive financial insight.

Next, we explored the data behind the cashflow story. Using a synthetic dataset representing weekly inflows and outflows, we profiled trends, seasonality, and account patterns. This phase focuses on trust — verifying that the data reflect real-world behavior and reveal early insight into forecast drivers. With the CPMAI process, each visualization and summary statistic builds stakeholder confidence that the upcoming model will rest on solid ground.

💼 Cash Flow at a Glance

This view shows how money moves through the organization each week—cash in from operations, cash out for expenses, and the resulting net position. Seeing all three together highlights liquidity trends, timing gaps, and volatility that drive forecasting needs. It grounds the MVP in the real business problem: giving finance leaders clear, forward-looking visibility into their cash rhythm.

📊 Understanding Cash Flow Variability

This chart shows the statistical distribution of weekly net cash balances, revealing whether liquidity tends to stay near break-even or swing between large inflows and outflows. A tight, centered shape suggests stability; wider or skewed distributions indicate volatility that the forecast model must capture. It helps stakeholders see the financial rhythm and risk profile before any modeling begins.

📆 Seasonal Cash Flow Patterns

This line graph reveals how average net cash changes across the calendar year. Peaks and dips highlight recurring cycles—such as payroll timing, supplier payments, or seasonal revenue shifts. Recognizing these patterns early ensures the model accounts for predictable fluctuations, strengthening forecast accuracy and CFO confidence.

🔁 Detecting Predictable Patterns

This chart measures how each week’s cash flow relates to prior weeks. Spikes at specific lags reveal recurring cycles or momentum—signals that the model can learn to anticipate. When autocorrelation is strong, it confirms that past behavior holds valuable clues about future liquidity.

🔄 Turning Patterns Into Predictors

Building on earlier insights, we engineered features that capture liquidity momentum, seasonality, and spending cycles. Thirteen-week rolling averages, week-of-year indicators, and recent inflow/outflow trends became key model signals. This phase bridges finance intuition with machine learning logic, turning business rhythm into forecastable data.

🔗 Exploring Relationships Among Features

This heatmap shows how the engineered features—such as lag values, rolling averages, and seasonal indicators—relate to one another. Strong correlations reveal patterns the model can leverage, while weaker or redundant links highlight areas for refinement. By visualizing these relationships before training, we ensure each feature adds distinct predictive value and avoids overlap that could distort results.

🔍 From Financial Signals to Forecasting Insight

Feature engineering work-flows bridge the gap between raw financial data and intelligent forecasting. Starting with weekly inflows, outflows, and net cash, we create structured signals that reveal momentum, seasonality, and operational rhythm. Lag features capture short-term memory; rolling averages smooth volatility; and calendar indicators highlight predictable cycles like payroll or month-end activity. Each transformation converts finance intuition into model-ready data—turning historical patterns into predictors of future liquidity.