What are the challenges of implementing AI-driven transaction monitoring systems and how do we overcome them?

Sumsub keeps getting questions from our followers about the specifics of regulatory compliance, verification, automated solutions, and everything in between. We’ve therefore decided to launch a bi-weekly Q&A series, where our legal, tech, and other experts answer your most frequently asked questions.

This week, our Transaction Monitoring Technical Manager, álvaro G. will talk about the current state of AI-driven transaction monitoring.

What are the challenges of implementing AI-driven transaction monitoring systems and how do we? overcome them?

Even though an automated transaction monitoring solution is a great tool for detecting suspicious fraudulent patterns, the technology is still far from perfect. In particular, traditional transaction monitoring solutions often struggle to spot complex behavioral patterns and can get outdated pretty soon when fighting constantly evolving fraudulent schemes.

That’s why it’s essential to employ the new technologies, such as AI-driven solutions, to minimize the gap between your security systems and the new forms of fraud out there. Compared to traditional tools, AI-driven solutions can change the entire field of transaction monitoring, making it more efficient and accurate while decreasing costs.

Possible challenges include:

• Over-reliance. While AI-driven transaction monitoring constantly adapts to new challenges, it still needs to be overseen by a human. Moreover, it needs to be adjusted and updated on a regular basis to ensure that the algorithms are evolving in the right direction.
• Adaptation of the regulatory system. Just like with any new technology, regulators need time to adapt to AI-driven solutions. At least at this point of time, #AI algorithms are complex and opaque, which makes it challenging to understand their decision-making process.
• Complex cases. AI can be trained well to spot anomalies and flag them, but it still needs to be monitored for complex cases. It’s necessary to stress the importance of implementing stringent AI rules and parameters and update them regularly. Otherwise, AI-driven algorithms risk missing a true positive or give too many false positive results.

To overcome all these issues a company needs to understand its priorities when setting up a transaction monitoring system. Then, based on these needs, it can employ a corresponding solution, which will be able to monitor and analyze large volumes of transactional data in real-time, comparing them against established risk profiles and predefined rules.

If overseen properly, AI-driven transaction monitoring solutions will lead to quicker, more efficient monitoring without causing any major challenges. To overcome such issues companies should properly convey to the AI system the exact kind of anomalies expected from the industry and types of customers.

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