AI in Regtech: Approaching the Monitoring Process with AI

By 2 minute read

In the previous blog in this series, we introduced regulatory technology (‘regtech’) and the impact that artificial intelligence (AI) is having on its growth in the context of onboarding. In this blog, we’re going to look at monitoring and how AI can help firms keep an eye on their clients’ activities.

As the financial sector continues to be disrupted by technology and more and more people get involved in activities like e-commerce, investing, cryptocurrency, and more, the monitoring requirements of firms within the financial industry are going to keep growing.

In recent years, there’s been an upward trend in big financial institutions such as banks looking towards regtech to improve their monitoring processes.

What is Monitoring in Regulatory Compliance?

Monitoring in the compliance process is the act of screening clients’ activities and transactions, and financial firms have a positive duty to do this stemming from laws such as the Money Laundering Regulations and the Criminal Finances Act. The main challenge with compliance here is that firms are facing increasingly complex regulations and legislation that have in turn created a complex regulatory environment to comply with.

To get monitoring right, firms must employ fine-tuned, efficient systems that are capable of monitoring data without throwing up lots of false positives. Monitoring must be detailed enough to capture potential illicit activity without being overly broad and generating false positives.

The complex regulatory environment, the need to deploy high-level monitoring processes, and pressure from regulators to get compliance spot on 100% of the time has led to more financial institutions relying on third-party regtech specialists to conduct their monitoring.

White paper: The use of AI in AML Transaction Monitoring

How is AI Used in the RegTech Monitoring Process?

AI can be utilized in the monitoring process in many ways, for example:

  • Transaction pattern detection: AI can process huge amounts of transactional data very quickly, much faster than a human operator. AI models can then learn from the data they process and begin to form an understanding of patterns and relationships, making the model better at flagging potential problem transactions and reducing false positives.
  • Reviewing regulatory changes: The regulatory environment is constantly evolving, particularly in relation to anti-money laundering (AML) efforts. In the EU, this can happen on both an EU-level and on a national level where Member States might take extra steps ahead of what has been stipulated by EU law. AI can be used to monitor for regulatory developments and report back to you with updates so that compliance teams can act in a timely manner.

Monitoring is something that you are legally obliged to do as a financial firm. While the regulatory environment is complex, AI-powered regtech solutions ease much of the burden and do a far better job of monitoring than your own internal processes ever could.

In the next part of this blog series, we’re going to look at the role AI is playing in the regtech detection process.

Ebook: How to Leverage AI for AML Compliance