The Application of Artificial Intelligence in Detecting Money Laundering in Kenyan Commercial Banks
Keywords:
Affordance actualisation theory, artificial intelligence, anti-money laundering, commercial banks, money launderingAbstract
Money laundering is a serious threat to financial stability and national security, made worse by systemic weaknesses. Traditional, rule-based anti-money laundering (AML) systems face challenges that often lead to data-collection issues and a high number of false positives. This study aimed to evaluate the use of artificial intelligence (AI) in AML operations within Kenya's commercial banking sector. It intended to address a significant gap in existing research by exploring how much AI is being used, the specific technologies involved, their perceived effectiveness, and the main challenges and opportunities for their application. Based on affordance actualization theory, this research used a practical mixed-methods approach. Data were collected through questionnaires from 76 respondents and detailed interviews with 15 participants. These included bank employees, officials from the Financial Reporting Centre, security agencies, and independent AML professionals. The findings show that AI applications in AML processes are diverse, with larger banks leading adoption. The study found a strong positive correlation between AI adoption and perceived effectiveness in combating money laundering. Based on these findings, the research recommends that leading banks share their success stories to motivate other institutions to adopt these technologies. It also suggests the need for clear guidelines and standards across the sector to ensure that AI is integrated consistently and effectively in the financial industry.
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