Finance
Financial Forecasting
Realize better financial forecasting using machine learning without requiring data duplication or concerns around data compliance.
Challenge
- Quarterly forecasting can be late as numerous divisions fail to submit their financial data on time leading to time overruns and stale data.
- Global financial data restrictions limit movement and sharing of some sensitive data prior to reporting periods.
- Delays in forecasts (and close) delay strategic decisions despite rapid changes in operating environments.
Solution
- Devron deployed on data within different divisions to gain global insights without requiring data duplication or requiring divisional input for analysis.
- Keeping data local across international divisions reduced the cost of centralization and concerns around data compliance.
- Forecasting time can decrease by as much as 45%, enabling organizations to respond to financial trends before, rather than after, competitors.
Outcomes
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Better Financial Outcomes
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More Stable Cash Flow
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Improve Model Accuracy
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Reduce Data Movement Costs
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