Make smarter financial decisions with AI-powered forecasting for revenue, expenses, cash flow, and budget planning that adapts to your business dynamics.
Static spreadsheet models fail to capture seasonality, market trends, and business drivers, leading to 20-40% forecast errors that derail strategic planning.
Manual cash flow forecasting misses timing of receivables and payables, causing liquidity crunches, missed opportunities, and emergency financing needs.
Annual budgeting consumes weeks of finance team time with spreadsheet consolidation, but budgets are obsolete within months as business conditions change.
Creating multiple forecast scenarios manually is so labor-intensive that most companies only produce 1-2 cases, missing risks and opportunities.
Our machine learning platform analyzes historical financials, business drivers, and market conditions to generate rolling forecasts with unprecedented accuracy and flexibility.
Instead of extrapolating historical revenue, we model the business drivers that create revenue: customer acquisition, retention, pricing, product mix, market conditions. ML models learn complex relationships between drivers and outcomes, adapting to changing business dynamics automatically.
Expense categories behave differently - some are fixed, others variable with revenue, some seasonal. Our platform automatically identifies expense patterns and applies appropriate forecasting methods for each category, from salaries to marketing spend to COGS.
Predict cash inflows and outflows with daily granularity by modeling payment terms, collection patterns, seasonal working capital needs, and capital expenditures. Critical for managing liquidity and optimizing cash deployment.
Ready to transform financial planning? Our ML forecasting platform reduces forecast error by 35% and cuts planning time by 60%.
Traditional financial forecasting relies on linear extrapolation, manual driver assumptions, and judgment-based adjustments. This works reasonably well in stable environments but breaks down when business dynamics change. ML forecasting offers fundamental advantages: (1) Non-linear relationships - captures complex interactions between variables, (2) Automatic pattern detection - finds seasonality, trends, and cycles without manual specification, (3) Multi-variate analysis - considers dozens of drivers simultaneously, (4) Continuous learning - updates as new data arrives, staying current.
Research on financial forecasting accuracy: ML methods reduce MAPE (Mean Absolute Percentage Error) by 30-50% compared to time series extrapolation, by 20-35% compared to multiple regression. For a €50M revenue company with traditional 25% forecast error, ML could reduce error to 15-18%, dramatically improving planning quality.
For recurring revenue businesses, we model cohort behavior: new customer acquisition, retention rates, expansion revenue, and churn. Models predict MRR/ARR by cohort, then aggregate for total revenue forecast. Key features: customer tenure, product usage metrics, support tickets, payment failures, competitive activity. Typically achieves 10-15% MAPE for 12-month forecasts. Integration with billing systems (Stripe, Chargebee) enables real-time forecasting.
Predict customer purchase frequency, basket size, and conversion rates. Features include: website traffic, marketing spend by channel, seasonality indicators, pricing, product launches, competitive landscape. We build separate models for new vs. returning customers. Time series models capture day-of-week and monthly patterns. For businesses with large catalogs, hierarchical forecasting predicts at category level then disaggregates to SKUs.
Revenue forecasting for consulting, construction, or project businesses requires modeling pipeline conversion and project timing. We use survival analysis to predict deal close probability and timing, then aggregate pipeline-weighted revenue. Features: deal stage, deal age, customer industry, deal size, assigned team, proposal metrics. Historical win rates by segment inform probability estimates. Works alongside CRM systems (Salesforce, HubSpot).
Customer order patterns, production capacity, backlog, and economic indicators drive forecasts. Features: customer order history, industry production indices, commodity prices, currency rates, lead times. For businesses with long sales cycles, we incorporate early indicators (RFQs, design wins) to predict future orders. Models account for order lumpiness and major customer concentration.
Expense forecasting requires understanding cost behavior and business drivers:
ML forecasting enables sophisticated scenario analysis: Base case (most likely), optimistic case (90th percentile outcomes), pessimistic case (10th percentile). Monte Carlo simulation generates thousands of scenarios by varying key assumptions, producing probability distributions for revenue, expenses, and cash. Sensitivity analysis identifies which drivers have largest forecast impact, focusing management attention on high-leverage variables.
A €75M ARR B2B SaaS company was struggling with revenue forecasting accuracy. Their spreadsheet-based model simply extrapolated historical MRR growth, resulting in 20-25% forecast error (MAPE) over 6-month horizons. Large variance between forecast and actuals made investor reporting difficult and planning unreliable.
We built a cohort-based forecasting system that models new bookings, expansion revenue, contraction, and churn separately. The system ingests data from Salesforce (pipeline), Stripe (billing), and their data warehouse (product usage). XGBoost models predict bookings based on pipeline stage, deal age, ACV, industry, and sales rep. Survival analysis models churn probability by customer segment. The platform generates rolling 18-month forecasts updated weekly.
Results: Revenue forecast MAPE improved from 21% to 13% for 6-month horizon (38% improvement), 12-month forecast MAPE of 18% vs. previous 28%. Cash flow forecasting accuracy improved from 27% to 16% MAPE. Finance team time spent on forecasting reduced 60% (from 80 to 32 hours monthly). Better visibility enabled earlier decisions on hiring and marketing spend, improving capital efficiency. Board and investor confidence increased significantly.
Minimum 2 years of monthly financials (P&L, balance sheet, cash flow) to capture annual seasonality. Ideally 3-5 years to see business cycles and growth patterns. For driver-based models, we also need related operational data (customers, usage, pipeline, headcount). Weekly or daily data improves short-term forecasting. If you're a newer company with limited history, we can augment with industry benchmarks, comparable companies, and incorporate your business plan assumptions into the models.
High-growth and changing businesses are actually where ML adds most value. Traditional models assume future mirrors past, which breaks during transitions. Our approach: weight recent data more heavily so models adapt to new patterns, incorporate leading indicators and business drivers rather than just historical financials, enable scenario planning for different growth trajectories, allow manual overrides for known changes (new product launches, market entry). We typically see better results during transitions than with extrapolation methods.
Integration is critical. We connect to your ERP (NetSuite, SAP, Oracle), CRM (Salesforce, HubSpot), billing systems (Stripe, Zuora), and data warehouse. ML forecasts can: (1) Replace existing forecast models while keeping your review and approval workflows, (2) Serve as starting point that finance teams refine with judgment, (3) Provide baseline for bottom-up department forecasts to compare against, (4) Export to Excel/Google Sheets for familiar analysis. Most clients adopt hybrid approach - ML for most line items, manual for unique situations.
Major disruptions challenge all forecasting methods. Our approach: rapid model retraining when patterns change (detect via forecast error monitoring), incorporate external economic indicators (GDP, unemployment, interest rates, industry indices) that signal macro changes, scenario planning with stress tests (recession, currency shock, supply chain disruption), and enable manual adjustments for unprecedented situations. The key is viewing ML as augmenting human judgment during uncertainty, not replacing it. Models help quantify impact scenarios even if point forecasts are unreliable.
Results vary by industry and current baseline, but typical improvements: 30-45% reduction in forecast error (MAPE) for companies using simple extrapolation, 20-30% improvement for those using driver-based spreadsheet models, 10-20% for sophisticated existing FP&A processes. Beyond accuracy, benefits include: 50-70% reduction in forecast cycle time, ability to generate multiple scenarios, daily/weekly forecast updates vs. monthly/quarterly, and better visibility into forecast drivers. We always run parallel forecasts for 3-6 months to demonstrate improvement before full adoption.
Transform financial planning with ML-powered forecasting that delivers better accuracy, faster insights, and confident decision-making. Get a free assessment of your forecasting improvement potential.
Based in Lund, Sweden • Serving businesses globally