About the GAIJ project

• Background

  Illicit financial transactions constitute a serious ethical challenge to the proper functioning of society. Various regulatory frameworks enforce banks, companies, and governments to keep checks on illicit financial flows, yet in gross amount, these still comprise a significant portion of the world's trade value. At the forefront of exposing malicious entities involved in non-sustainable activities are investigative journalists. Yet assessing financial data in a digital world is a strenuous task.

  GAIJ – Graph-bound Artificial Intelligence Journalism – is a project centred on utilising modern open-source Large Language Models (LLMs) to classify illicit transactions in financial and tax records. The project will develop a prototype open-source Artificial Intelligence (AI) model that examines and classifies transaction data, creating a graph of suspicious interactions between companies.

• Aims

  The working prototype, GAIJ – Graph-bound Artificial Intelligence Journalism – is a project centred on utilising open-source Large Language Models to classify illicit transactions in financial and tax records.

  The target of this pilot project is to develop a prototype open-source python-based AI model that examines and classifies transaction data and casts these interactions onto a graph of interactions, used to trail suspicious activities by financial agents.

  The project is developed on the intersection between Artificial Intelligence research and investigative journalism. The primary target is to develop a tool that can help journalists, academics, and the public, in identifying illicit transactions of companies.

• Participating researchers