Luo, XubeiRajasekaran, Arvind BalajiScruggs, Andrew Conner2025-11-052025-11-052025-11-04https://hdl.handle.net/10986/43937Effective monitoring of development aid is the foundation for assessing the alignment of flows with their intended development objectives. Existing reporting systems, such as the Organisation for Economic Co-operation and Development’s Creditor Reporting System, provide standardized classification of aid activities but have limitations when it comes to capturing new areas like climate change, digitalization, and other cross-cutting themes. This paper proposes a bottom-up, unsupervised machine learning framework that leverages textual descriptions of aid projects to generate highly granular activity clusters. Using the 2021 Creditor Reporting System data set of nearly 400,000 records, the model produces 841 clusters, which are then grouped into 80 subsectors. These clusters reveal 36 emerging aid areas not tracked in the current Creditor Reporting System taxonomy, allow unpacking of “multi-sectoral” and “sector not specified” classifications, and enable estimation of flows to new themes, including World Bank Global Challenge Programs, International Development Association–20 Special Themes, and Cross-Cutting Issues. Validation against both Creditor Reporting System benchmarks and International Development Association commitment data demonstrates robustness. This approach illustrates how machine learning and the new advances in large language models can enhance the monitoring of global aid flows and inform future improvements in aid classification and reporting. It offers a useful tool that can support more responsive and evidence-based decision-making, helping to better align resources with evolving development priorities.en-USCC BY 3.0 IGOFOREIGN AIDCLASSIFICATION METHODSCLUSTER ANALYSISMODELING AND ANALYSISWorking PaperWorld Bank