Artificial intelligence and machine learning in combating illegal financial operations: Bibliometric analysis

Abstract

Money launderers and corrupt entities refine methods to evade detection, making artificial intelligence (AI) and machine learning (ML) essential for countering these threats. AI automates identity verification using diverse data sources, including government databases and social media, analysing client data more effectively than traditional methods. This study uses bibliometric analysis to examine AI and ML in anti-money laundering and anti-corruption efforts. A sample of 746 documents from 477 sources from Scopus shows a 14.33% annual growth rate and an average document age of 3.51 years, highlighting the field's actuality and rapid development. The research indicates significant international collaboration in documents. The main clusters of keywords relate to the implementation of AI and ML in (1) avoiding fraud and cybersecurity, (2) AML compliance, (3) promotion of transparency in combating corruption, etc. Addressing ethical concerns, privacy, and bias is crucial for the fair and effective use of AI and ML in this area.