Combining Digital Forensic Practices and Database Analysis as an Anti-Money Laundering Strategy for Financial Institutions

Abstract

Digital forensics is the science that identify, preserve, collect, validate, analyse, interpret, and report digital evidence that may be relevant in court to solve criminal investigations. Conversely, money laundering is a form of crime that is compromising the internal policies in financial institutions, which is investigated by analysing large amount of transactional financial data. However, the majority of financial institutions have adopted ineffective detection procedures and extensive reporting tasks to detect money laundering without incorporating digital forensic practices to handle evidence. Thus, in this article, we propose an anti-money laundering model by combining digital forensics practices along with database tools and database analysis methodologies. As consequence, admissible Suspicious Activity Reports (SARs) can be generated, based on evidence obtained from forensically analysing database financial logs in compliance with Know-Your-Customer policies for money laundering detection.