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.
Challenges of Current Transaction Monitoring Processes
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.
Why Optimize Your Transaction Monitoring?
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:
- Ability to accurately and quickly process and analyze larger sets of data.
- Increased efficiency in detecting suspicious activities and enhanced focus on a risk-based approach (RBA).
- Improved client segmentation and risk assessment based on advanced insights into transactional behaviors.
- Improved cost-efficiency with a more intelligent and risk-based approach to reduce false positives.
- Sophisticated information management and data analytics can better inform governance oversight.
How To Optimize Transaction Monitoring for AML Compliance
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:
- Report suspicious activity immediately, and file a Suspicious Transaction Report (STR) and Suspicious Activity Report (SAR) within 35 days of the alert. “As a result, the CBUAE considers defensive STRs or SARs as indicative of an inefficient transaction monitoring system and an LFI’s weak system of internal controls,” notes Deloitte.
- Design TM detection rules and scenarios to identify and flag suspicious patterns. Segmentation of risk-based customers and products is especially useful. Performing pre-implementation testing of TM rules will also ensure compatibility.
- Examine TM systems and processes for greater functionality. Detection scenarios should be regularly updated for relevancy and assumptions. Rule threshold values should be also calibrated at regular intervals.
- Regularly test and validate data integrity, accuracy and quality at least every 12 to 18 months.
Additional aspects that should be considered for the optimization of transaction monitoring for AML efficiency include:
- Scenario Tuning: Implementing a systematic scenario tuning cycle facilitates a more targeted threshold setting based on advanced analytics of historical information so that false positives can be reduced; identifying redundant and ineffective scenarios, and success factors based on effective scenarios.
- Improved Alert Scoring: The generation of true positives through finely tuned scenarios will promote effective alert scoring.
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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