Methodology
Accurate forecasting and data validation across global markets and corporate finances demand more than traditional tools. Complete Intelligence bridges that gap combining global economic and market data with advanced machine learning.
A Globally Integrated, Data-Rich Model
Our global model reflects how actions in one market, country, or sector impact others, capturing interconnected movements across currencies, commodities, equity indices, stock markets, economic indicators and internal customer data.
We source data from national statistical agencies, multilateral institutions, exchanges and other public sources, covering 50 countries, hundreds of industry sectors, and all major macro and market indicators going back to 2010.
The process begins with rigorous data screening, repair, and validation. We apply multi-layered analyses to assess how data should behave, then model relationships across economic, market, and industry variables using our proprietary machine learning engine. This powers predictive intelligence for prices, costs, budgets, and revenues.
Corporate Finance: The Human-in-the-Loop Advantage
For AuditFlow and BudgetFlow, we employ a rigorous human-in-the-loop (HITL) methodology. Unlike our automated market feeds, corporate data requires deep contextual understanding.
Ingestion
AI maps your accounts data.
Detection
Algorithms identify outliers, relationships and best ensemble for each account line.
Validation
Your experts review these outputs, verifying that anomalies are genuine and forecasts are meaningful to your business performance.
This hybrid approach eliminates the ‘black box’ risk common in pure AI solutions.
Ensemble Modeling for Smarter Forecasts
We use an ensemble methodology that analyzes billions of data points and tests millions of functions for each forecasted item. This creates a dynamic model that mirrors the global economy, markets, industries, and companies.
By combining multiple forecasting approaches—both technical and fundamental—we generate thousands of scenarios across different historical windows. Each iteration improves accuracy by adapting to market context, delivering robust, continuously learning forecasts at the line-item level.
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