Financial Forecasting & Planning

Traditional financial forecasting relies on time-series models that assume historical patterns continue unchanged, missing structural shifts and causal drivers. Our causal forecasting methodology identifies what actually drives financial metrics, enabling forecasts that adapt to changing business conditions.

We applyCausal Machine Learningto solve complex business challenges.

We combine causal discovery algorithms with econometric modeling to identify the true drivers of revenue, costs, margins, and cash flow. Rather than fitting patterns to historical data, we model the mechanisms that connect operating metrics to financial outcomes. This includes scenario analysis that accounts for how interventions cascade through your business: how will an increase in marketing spend affect revenue given competitive responses? What's the lagged impact on customer lifetime value? How do changes in pricing interact with unit economics? Our approach leverages [high-dimensional econometric methods](/research#high-dimensional-metrics) to handle complex business environments.

CFOs using our platform can substantially reduce forecast error compared to traditional methods and gain actionable scenario insights for planning. Companies can confidently model the financial impact of strategic decisions before execution. During market disruptions, our causal models adapt faster because they're anchored to business mechanisms rather than historical correlations.

Interactive dashboards let finance teams stress-test assumptions, explore what-if scenarios, and communicate forecast confidence ranges to the board with statistical rigor.