Award-Winning ML Solution Delivers 80% Reduction in Manual Forecasting Effort for European Leader 

Client Context 

A leading European corporation, renowned for its forward-looking treasury operations, sought to transform the way it forecasted cash flows across hundreds of cost centers. Their strategic goal was to move from labor-intensive, manual predictions to a scalable, automated system that could boost both accuracy and efficiency. 

The Challenge: Manual Burden, Limited Precision 
The client’s treasury function was struggling to achieve this goal due to a dual burden: 

• Resource Intensive: Monthly predictions required more than 350 man-hours to generate 12-month rolling forecasts for hundreds of individual cash flow time series. 
• Low Accuracy: The manual process produced results that fell short of the desired precision, hampering strategic treasury decisions. 

The treasury team recognized that automation was needed — not just to save time, but to extract reliable, actionable forecasts for business agility.  

The InXiteOut Approach 

Working closely with the client’s treasury team, InXiteOut mapped out the end-to-end forecasting process, identifying the core business drivers behind cash flows. The solution was designed in three stages: 

Feature engineering 

Leveraging deep domain expertise, a robust set of predictive features was curated for modeling: 

• Historical Trends: Cash flow data for every cost center. 
• Transactional Data: Volume, value, and timing of sales and purchase invoices. 
• Macroeconomic Indicators: GDP, wage growth, and inflation. 
• Process Factors: Tax payment mechanisms and reconciliation timing. 

ML model development 

InXiteOut designed a sequential modeling framework: 

• Outlier Detection: First, a sophisticated outlier detection model filtered erratic patterns triggered by one-off events (like acquisitions or process shifts) to ensure data integrity. 
• Cash Flow Prediction: Next, a multivariate regression-based model predicted future cash flows based on the engineered features, unlocking reliable monthly forecasts. 

This approach enabled both the detection of out-of-pattern anomalies and the generation of actionable, granular forecasts for each cost center.  

Deployment and integration 

To make the forecasts actionable, the solution was deployed as an automated pipeline. The system was integrated with the client’s existing treasury tools, sending the validated monthly forecasts directly to the finance team. We also developed a custom performance dashboard for leadership, providing a real-time view of forecast accuracy and cash flow trends. 

Technology stack used 

• Azure ETL Ecosystem (Data Lake, Data Factory) 
• Dataiku     
• Power BI  

Benefits Delivered 

The solution delivered transformative results, achieving all the client's goals and earning industry-wide recognition: 

• 80% Reduction in Forecasting Workload: The automated solution reduced the monthly forecasting workload by 80%, freeing the treasury team for higher-value strategic analysis. 
• ~30% Improvement in Forecast Accuracy: The solution improved forecasting precision by up to 30%, enabling sharper, more confident treasury decisions. 
• Award-Winning Industry Recognition: The project received the “Excellence in Treasury” award from the European Association of Corporate Treasurers, recognizing the transformation as best-in-class.