In today's rapidly evolving global economy, financial institutions encounter numerous challenges in detecting and preventing illicit activities. Over the years, transaction monitoring has experienced an evolution, driven by technological advancements, regulatory requirements, and the changing nature of financial crimes.
MANUAL TRANSACTION MONITORING – THE EARLY DAYS
In the early days, transaction monitoring was primarily conducted manually, which involved human intervention and analysis rather than using an automated system or software. It was a process that was time-consuming, rudimentary, and susceptible to human error. Involvement of multiple parties and diverse financial products have made manual transaction monitoring more difficult, forcing financial institutions to implement rules-based systems to monitor and flag suspicious transactions or activities. Despite its limitations, manual transaction monitoring provided flexibility, contextual understanding, investigative depth, and continuous improvement, among many other advantages that an automated system could not provide at the time. This method also relied on human analysts to review account activity and identify suspicious transactions allowing them to make informed decisions about whether a transaction warranted further investigation. An analyst could detect patterns, scenarios, and/or irregularities that an automated system could have missed. More importantly, manual transaction monitoring provides feedback to inform and improve automated transaction monitoring systems, thus making manual reviews and human input vital components of any successful transaction monitoring program.
FINANCIAL INTELLIGENCE
Financial intelligence has evolved in recent years due to technological advancements. These advancements have revolutionized the way we access, analyze, and utilize data. Criminals have also found new opportunities to exploit the latest technologies, forcing institutions to optimize transaction monitoring solutions. As a result, transaction monitoring has undergone shifts to keep up with technology demands. Automation and machine learning algorithms have revolutionized financial intelligence by automating repetitive tasks and analyzing massive datasets with speed and accuracy to identify patterns and ensure comprehensive monitoring and detection of suspicious activity. This allows financial institutions to stay ahead of evolving risks and better protect themselves and their customers from illicit activities.
AUTOMATED TRANSACTION MONITORING – THE NEW NORMAL
Technology plays a significant role in the evolution of financial crime. It has opened new and easier avenues for criminals to conduct illicit activity. Financial crime has become more complex, creating a widespread desire to embrace the latest advances in technology across transaction monitoring. Consequently, financial institutions have implemented methods to identify suspicious activity, assess risk levels, and make predictions about future customer activity and patterns. As technology and financial crime have advanced, financial institutions have started implementing more sophisticated automated transaction monitoring systems. These systems have been integrated to improve transaction monitoring alerts, minimize false positives, reduce the workload on compliance teams, and define red flags for investigations, creating a more efficient structure as well as easily adapting to changing industry typologies and best practices. There is an evergrowing list of technologically advanced platforms to detect suspicious activity easier and sooner, improve the quality of alerts, and increase efficiency in identifying new patterns of suspicious activities.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING – NOW AND LOOKING TO THE FUTURE
Advanced technology such as artificial intelligence (AI) and machine learning (ML) play an important role in transaction monitoring in various industries, including finance, banking, and e-commerce. Both can analyze massive amounts of data, detect anomalies that may indicate fraudulent or suspicious behavior, adapt to new trends and patterns, improve the accuracy of detecting suspicious transactions over time, and learn from historical patterns. The use of data, analytics, and robotics has renovated the current and future state of financial crime monitoring. AI and ML algorithms can learn patterns and behaviors from historical data to identify irregularities in transactions. These algorithms can adapt and improve their detection capabilities over time as they encounter new data. They can also be trained on updated data, allowing transaction monitoring systems to be effective in the ever-changing fraud techniques. However, real people will always be needed to make sure the software is working correctly and to continue feeding the system the correct data.
CONCLUSION
The evolution of transaction monitoring has shifted from manual processes to advanced, automated systems that influence technology, data analytics, AI an ML. A combination of manual and automated transaction monitoring is recommended for a comprehensive and efficient monitoring program. As the Financial Action Task Force (FATF) outlines in their paper Opportunities and Challenges of New Technologies For AML/CFT, "Combining the efficiency and accuracy of digital solutions with the knowledge and analytical skills of human experts produces more robust systems that can effectively respond to AML/CFT requirements."1
Footnote
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