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AI-Driven Transformation in Romanian Farming: A Case Study
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Cities & Municipalities
- Education
Use Cases
- Demand Planning & Forecasting
- Movement Prediction
Services
- Data Science Services
The Challenge
The Rural Investments Financing Agency (AFIR), a Romanian government agency, is responsible for supporting farmers and companies to access grants and an average of 2 billion euros yearly of European Union (EU) funding for rural development projects. However, a vital internal forecasting task to manage these grant projects required a lot of manual intervention by AFIR’s employees. This process was not only slow but also prone to inaccuracies. The organization was keen on increasing the accuracy of spending and financing forecasts, as this would not only help its users but also boost its own operational efficiency. The process was paper-heavy and complex, and according to Daniel Ifrim, IT Director at AFIR, it was 'far from optimized'. The team needed a solution that would transform this process into a fully paperless and online system, and reduce the time taken to produce a report with spending and financing forecasts.
About The Customer
The Rural Investments Financing Agency (AFIR) is a Romanian government agency based in Bucharest. It is the main government agency supporting Romanian farmers and companies to access grants and an average of 2 billion euros yearly of European Union (EU) funding for rural development projects. AFIR is an important part of the Romanian government, with its primary role being to get funding and financial support into the hands of Romanian farmers and other key stakeholders who need resources to properly develop and care for the countryside. The organization is large, with between 1,000 and 9,999 employees.
The Solution
AFIR decided to experiment with artificial intelligence (AI) after local Microsoft partner Genisoft responded to a tender process. Genisoft proposed a completely new prediction mechanism for payments, which was delivered on a combination of Microsoft Azure Synapse Analytics and Azure Machine Learning, Azure SQL, and Microsoft Power BI. This AI-based payment prediction mechanism offered many improvements, especially around more accurate forecasting. The AI-driven prediction removed the risks of errors that could impact the report accuracy. The transformation process took several months, but the result was a fully paperless and online system. The new system not only increased the accuracy of the spending and financing requirements forecast but also reduced the time taken to produce a report from ten days to just ten minutes.
Operational Impact
Quantitative Benefit
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