The technology behind Complete Intelligence is built on three pillars:
A globally integrated machine learning model
A data-driven process without human intervention in the output
A simple means of interfacing with the platform
In general terms, how does the forecasting process work for RevenueFlow and CostFlow?
Receive historical financial data from an enterprise
Pre-Process
Data Validation
Review for missing data
Review for consistency of data
Review for duplication of data
Check formatting of data
Data Repair
Crosswalk line items
Interpolation of data
Elimination of duplicates
Correct formatting
Segregation of data
Forecastable
Not forecastable
Forecastable after repair
Forecasting
Back-test individual algorithms within historical data of individual line item
Back-test combinations of individual algorithms within historical data of individual line item
Identify best combination of individual algorithms of back-test forecasts
Forecast future periods of individual line item based on best combination of individual algorithms identified
Repeat the above for each individual line item to be forecasted
Post-Process
Test forecast of individual line items for anomalies
Outliers
Positive and Negative
Straight line / Identical slope
Run Forecast process again for individual line items identified
Issue Forecast Data
Forecast data is appended to historical data (same format and file type)
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