Cracking the Code: Enhancing Development finance understanding with artificial intelligence
This work addresses the need for improved analysis of development finance data for donors, recipients, and researchers, though it is incremental as it applies existing AI methods to a specific domain.
The research tackled the problem of analyzing development project narratives in the OECD CRS dataset, which lacks clear project purposes due to donor self-reporting, by using NLP and BERTopic to categorize and label projects, revealing hidden topics to better understand donor priorities and development funding.
Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.