Leveraging LLMs to Streamline the Review of Public Funding Applications
This work addresses efficiency problems for government agencies handling large volumes of funding applications, but it is incremental as it applies existing AI methods to new real-world data.
The paper tackled bottlenecks in evaluating public funding applications by deploying AI-assisted evaluation in two government initiatives, resulting in a 20.1% increase in reviewer productivity and over 2 months reduction in total evaluation time.
Every year, the European Union and its member states allocate millions of euros to fund various development initiatives. However, the increasing number of applications received for these programs often creates significant bottlenecks in evaluation processes, due to limited human capacity. In this work, we detail the real-world deployment of AI-assisted evaluation within the pipeline of two government initiatives: (i) corporate applications aimed at international business expansion, and (ii) citizen reimbursement claims for investments in energy-efficient home improvements. While these two cases involve distinct evaluation procedures, our findings confirm that AI effectively enhanced processing efficiency and reduced workload across both types of applications. Specifically, in the citizen reimbursement claims initiative, our solution increased reviewer productivity by 20.1%, while keeping a negligible false-positive rate based on our test set observations. These improvements resulted in an overall reduction of more than 2 months in the total evaluation time, illustrating the impact of AI-driven automation in large-scale evaluation workflows.