The United Nations Office on Drugs and Crime (UNODC) reports that the amount of money laundered globally per year is approximately 2% - 5% of global gross domestic profit (GDP), which translates to $800 billion-$2 trillion. The expanding interconnectedness and rapid digitization of today’s financial system certainly provide more opportunities for financial crimes to flourish. A robust and effective transaction monitoring (TM) solution for Anti-Money Laundering (AML) security has never been as crucial for financial institutions as it is today. In order to adapt to the ever-changing risk climate, transaction monitoring for AML must be adequately optimized for all contingencies.
Many financial institutions are adopting artificial intelligence (AI) and machine learning (ML) technologies to upgrade their transaction monitoring processes. However, most financial institutions (FIs) are still dependent on traditional, rule-based TM systems and are, therefore, largely ineffective against sophisticated criminal activities.
For example, traditional TM systems still rely on static data and data collection as well as outmoded data modeling and storage methodologies and technologies. In addition, the preset rules they’re based on allow for limited configuration. As a result, such systems are ill-equipped to process more complex and varied transaction data.
Ineffective data processing leads to poor data quality, which further results in the inability of a TM system to detect potentially illicit financial transactions. Poor data quality is also the primary cause of immensely high rates of false positives, which significantly increases the operational risk and cost of AML compliance and takes away valuable resources needed to properly implement and maintain TM processes. These pitfalls of current TM systems highlight the urgent need to optimize transaction monitoring for AML efficiency.
Actionable client data and insights are more accessible than ever before. The enormous value of such information to enhance Know Your Customer (KYC) processes concerning transaction monitoring for AML can only be leveraged through the optimization of legacy TM systems. Below are the benefits of said optimization:
There are various ways to optimize transaction monitoring for AML efficiency. Improving and strengthening existing TM systems can be done by adopting AI and ML technologies, introducing intelligent automation (IA) and implementing continuous monitoring processes.
One of the most recent regulatory guidelines for TM optimization is based on the Central Bank of the United Arab Emirates (CBUAE) issued guidance for licensed financial institutions (LFIs) AML compliance. Deloitte’s key takeaways from these guidelines are as follows:
Additional aspects that should be considered for the optimization of transaction monitoring for AML efficiency include:
The many opportunities for TM optimization make it possible for FIs to stay one step ahead of criminals and provide them with advanced tools to mitigate risks and address illicit activities more effectively. When choosing an optimization option, a financial institution must carefully consider which approach is most compatible with its systems and structures.
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