It's Monday morning, and the alert queue has grown over the weekend. Among the sea of alerts, there are a few cases that need your full attention, but finding them is the difficult part.
This is why Trapets has started a collaboration with DNB Carnegie Investment Bank to develop the next generation of transaction monitoring and KYC solutions by combining rule-based and AI-driven detection, to help you identify and prioritise cases with greater confidence.
The collaboration brings together two types of expertise: DNB Carnegie contributes with practical experience in KYC and transaction monitoring, along with lessons learned from developing AI-driven solutions for financial crime prevention in Nordic banking.
Trapets contributes with technology and specialist knowledge in financial crime prevention.
The Instantwatch architecture is well suited to support AI-enabled compliance processes by ensuring that KYC, screening, and transaction monitoring work from a consistent and connected data foundation.
This creates strong technical prerequisites for reliable analysis, automation, and assisted decision-making enhanced by AI across the platform.
Working closely with customers has always been an important step in how we develop our platform.
For more than 20 years, the knowledge and feedback from compliance professionals have guided how Instantwatch evolves. As a customer and partner of over a decade, we're delighted to bring DNB Carnegie's experience directly into the development work.
The challenge: a long list of alerts
Many AML professionals encounter a common challenge: a long list of alerts, many turning out to be low risk, that need full attention to identify the high-risk ones.
The collaboration focuses on this problem by combining rule-based methods with AI-driven detection. The aim is stronger risk identification and better support for prioritisation, which can contribute to more efficient investigations.
Combining rule-based and AI detection
Rule-based detection gives clear, predictable logic. You can see which scenario triggered an alert and why, which makes it easier to access the case and document your decision.
AI-driven detection can identify patterns across large volumes of data that fixed thresholds may miss. Combining both methods gives the alert more context, and the cases with the highest risk can be easier to spot.
Clear logic behind every alert
An alert only helps if you understand it, act on it, and explain your decision afterwards.
That's why this collaboration keeps requirements for transparency, interpretation, and governance in place.
AI is applied in areas where it can deliver clear and verifiable value, so the reasoning behind an alert stays visible to those working on the case.
“Success should not be measured solely by the number of alerts generated, but by the ability to identify and prioritise relevant risks. The future lies in different detection methods complementing one another, with AI used in areas where it can create clear and verifiable value, while maintaining requirements for transparency, interpretation, and governance,” says Gabriella Bussien, CEO of Trapets.
Want to learn more? Read how DNB Carnegie uses Trapets for comprehensive financial crime prevention or book a demo to see how Trapets Transaction Monitoring supports alert review and investigation today.