In recent times, real-time anti-money laundering (AML) has become a topic of interest for financial institutions. As transaction volumes—and therefore fraud—have increased in the last few years post-COVID, financial institutions have experienced a surge in regulatory expectations to be more effective in preventing suspicious transactions from being processed in the first place. Enter real-time AML.

But what exactly is real-time AML, and where did it come from?

Real-time AML enables financial institutions to monitor payments and customer activity instantly and monitor transactions in near real-time, such as via immediate screening and micro-batches. This substantially reduces the time between initial action and risk-based response, thereby allowing faster detection, decisions, and prevention of suspicious activity.
Ultimately, real-time AML aims to make financial crime harder to start and perpetuate within financial institutions by detecting and addressing suspicious behavior as it happens.
Many forces are driving the move to real-time AML. Two which are worthy of note are the acceleration of financial crimes and scams during and after COVID, which has invoked certain regulators to act, and the push to be more effective in how crime is weeded out and stopped in its tracks.

• Regulatory push: While regulators globally are looking at real-time AML, European regulators, particularly in the Nordics, are leading the charge. Some Nordic regulators have already instructed regional financial institutions to examine how to deploy the technology in the coming years. A recent Financial Action Task Force (FATF) report, “Opportunities and Challenges of New Technologies for AML/CFT,” references and encourages real-time capabilities, with FATF concluding that technology can “identify and manage money laundering and terrorist financing (ML/TF) risks more effectively and closer to real-time.”
• More effective prevention of financial crime: The rise of account takeover (ATO), dark web activities, anonymous currencies like Bitcoin, and instant payments all pressure financial institutions to speed up and fine-tune their response to risky behaviors. These responses involve using people, processes, and technology to better detect these behaviors, so the ‘intelligence’ learned can detect and prevent similar patterns from occurring again and again, in close to real-time.
• Move to integrated risk management: The interplay between AML and fraud is again drawing attention to similarities in process, detection, and system components to service both anti-crime lenses. Architectural stacks with systems that benefit both AML and fraud teams are one way to achieve this - reducing costs, maximizing resourcing, and architectural and data efficiencies overall. While fraud demands a real-time response, until now, AML batch-based approaches have led to different system requirements. Shifting towards real-time AML creates an opportunity to adopt a comprehensive risk-based real-time approach.

The move to real-time AML is not without its challenges. Whether you have concerns over having a clear definition of real-time, high costs to revamp architecture/technology, or how to handle the increased volume of alerts in a short timeframe, there is no easy quick fix to address the impact real-time AML will have on compliance teams. While regulators are encouraging a real-time approach, the specifics on how to address these challenges remain with financial institutions and their technology providers to work out currently.
It is safe to say this new wave of real-time AML interest is real and will likely move forward in the years ahead. Will you be ready? Will you understand the impact on AML compliance teams in adopting real-time AML? That is to say, can you handle more alerts more frequently? Can you use your existing technology to overcome the challenges?

One answer to addressing this is to incorporate AI and machine learning capabilities. Intelligent solutions that learn, scale, and support will be key in both reducing false positive alerts and streamlining investigation workflows – empowering investigators to address the higher risk activity sooner and make real-time effective and efficient.

Want to know more about how we can help you address your needs and what we’re doing for real-time AML, contact SymphonyAI Sensa-NetReveal today.

About the author
Meghan Palanza is an AML product manager at SymphonyAI Sensa-NetReveal. She is responsible for determining the product roadmap for AML, guiding product design, and interacting with customers on all matters AML. Prior to her 11 years at NetReveal/BAE Systems, Meghan was with Fidelity Investments for 15 years as a senior investigator. She is CAMS and CGSS certified.