A Model for Harnessing Financial Intelligence Systems for Money Laundering Prevention: A Systematic Review of Technological and Regulatory Approaches

Abstract

The study aims to develop a comprehensive model for harnessing financial intelligence systems to prevent money laundering in Zimbabwe. It systematically reviews the technological and regulatory approaches employed within the country‘s anti-money laundering framework, focusing on key challenges such as economic instability and regulatory deficiencies. The methodology involves a detailed analysis of both technological tools (such as transaction monitoring systems and data analytics) and regulatory measures (including compliance requirements and enforcement mechanisms). Stakeholders were selected based on their involvement in anti-money laundering (AML) efforts, with criteria including their roles in regulatory authorities, financial institutions, law enforcement agencies and technology providers. The study targeted 40 stakeholders involved in anti-money laundering efforts in Harare, the capital city and utilised random sampling to select participants from various sectors. Ultimately, 32 respondents contribute to the research. The findings highlight significant issues, with regulatory deficiencies such as inconsistent enforcement of laws and lack of clear guidelines and economic instability, including hyperinflation and currency volatility, identified as the most pressing challenges. Recommendations include the integration of advanced technologies like artificial intelligence (AI) and machine learning, comprehensive training programmes for stakeholders and centralised information sharing to improve collaboration. The study underscores the need for an integrated model that enhances regulatory frameworks, technological infrastructure and institutional capacity to effectively combat money laundering in Zimbabwe.