Cracking the Code: Enhancing Development finance understanding with artificial intelligence

TL;DR

This paper introduces a novel AI-based NLP approach using BERTopic to analyze and categorize development project narratives, revealing hidden topics and improving understanding of donor priorities in development finance.

Contribution

It presents an innovative combination of NLP and machine learning techniques to categorize and label development projects from large narrative datasets, enhancing insight into development finance.

Findings

Revealed hidden topics in development project narratives

Improved understanding of donor priorities

Demonstrated effectiveness of BERTopic in this context

Abstract

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.