Cross-border AML transaction monitoring with Finom

THE CLIENT

Finom: Monitoring transactions in a pan-European context

Finom is an innovation-driven fintech whose mission is to simplify the lives of small and medium businesses and freelancers who are the economic backbone of Europe. Since its founding in 2019, Finom’s financial services—which include payments, electronic invoicing, expense management, and accounting integrations—have facilitated over €4 billion in annual transactions in Germany, France, Italy, Belgium, and the Netherlands. Finom’s goal: to offer best-in-class financial services in every country within the EU.

THE CHALLENGE

Encountering the limitations of rule-based systems

But it's precisely when entering new operating environments that new threats arise. "As a new kid on the block, we are particularly exposed to various types of fraudsters and other scammers trying to exploit our systems, trying to find how they can use us for their benefit," said Kseniia Kutyreva, Head of Risk and Compliance at Finom. "So the early days in every country where we launch is when our risk engine in the CDD domain—and our detectors and alerts in transaction monitoring—are really put to the test."

The main challenge proved to be an existing system that couldn't keep up with unique activity patterns. Concerning behavior in one country may be normal in another, yet the rules-based system generated an alert whenever a transaction filled a specific condition. As a result, the Finom team struggled to cope with false positives—95% of the time—versus genuinely unusual activities worth investigating. Analysts ended up spending most of their efforts where they weren't needed.

This also risked wasting the time and patience of customers who unwittingly triggered alerts in the course of normal activities, resulting in requests for information where they were not in fact needed.

OUR SOLUTION

New detections put new risks into context

"For years I was speaking with different providers and [none] ever truly aligned with what we were searching for," said Kseniia. But in a chance demonstration of Resistant AI's Transaction Forensics, she found "a scientific, methodological approach to detecting suspicious activity in transaction monitoring." Better yet, she added, "The fact that we could use our alert management system was a huge advantage."

As an overlay onto Finom's existing transaction monitoring tool rather than an entirely new system, Resistant AI began digesting Finom's data streams in mid-2022 after only a few weeks of implementation. Rather than trying to tinker with inflexible rules, the ensembles of models that make up each Transaction Forensics detector use statistical anomalies to uncover previously unseen relationships and behaviors that are out of the ordinary for certain customers. Not only do Resistant's detectors bring truly unusual behavior to the fore, they do so along with detailed descriptions and priority indicators that contribute to swift investigations. All the while, analysts maintain a single workspace—and a single source of trust.

These insights contributed to better and faster investigations straight away: "When analysts have a clear idea of what they're looking for, when they have sufficient context and clear explanations of why an alert was triggered, they won't waste their time where it's not necessary," Kseniia explained.