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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09674 |
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| _version_ | 1866909836669616128 |
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| author | Marques, Joao D. S. Duarte, Andre V. Carvalho, Andre Rocha, Gil Martins, Bruno Oliveira, Arlindo L. |
| author_facet | Marques, Joao D. S. Duarte, Andre V. Carvalho, Andre Rocha, Gil Martins, Bruno Oliveira, Arlindo L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging LLMs to Streamline the Review of Public Funding Applications Marques, Joao D. S. Duarte, Andre V. Carvalho, Andre Rocha, Gil Martins, Bruno Oliveira, Arlindo L. Computers and Society Artificial Intelligence 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. |
| title | Leveraging LLMs to Streamline the Review of Public Funding Applications |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2510.09674 |