By Tamás Kádár, CEO at SEON
The global financial crime landscape is evolving at rapid speed. Criminals no longer rely on a single playbook. Instead they combine fraud, cybercrime and money laundering into multi-vector attacks that exploit the gaps between fraud prevention and anti-money laundering (AML) functions.
This fragmentation is a major weakness. Research shows only 2% of global financial crime is properly detected, as organisational silos and disconnected technology create blind spots. In practice, attackers can move from account takeover (ATO) to laundering funds through mule networks within hours – long before traditional defences catch up.
Why Point Solutions Fall Short
Most fraud prevention or AML AI models are built on narrow data sets and optimised for isolated use cases. A system trained only on device data or transaction histories may catch anomalies in its lane but misses coordinated, cross-functional threats.
Some institutions try to bridge the gap by manually combining fraud and AML insights with analytics teams. But data lags, errors and high maintenance costs make this approach unsustainable in an environment where criminal tactics evolve daily. Resilience requires a different model.
AI That Sees the Whole Picture
Advanced financial institutions are adopting AI architectures that use signals across the entire user journey – device and email data, IP addresses, behavioral biometrics and payment metadata – through a single decisioning layer.
This horizontal intelligence connects dots that siloed systems cannot. It shifts detection from isolated incident monitoring to contextualized analysis of coordinated campaigns. For organisations managing rising transaction volumes and investigative workloads, this unified view accelerates resolution and strengthens defenses.
Network Analysis to Reveal Hidden Relationships
The most damaging financial crime schemes – synthetic identities, mule operations and fraud rings, depend on networks of accounts, devices and geographies. Traditional systems focused on individual entities rarely surface these connections.
Network analysis, powered by horizontal intelligence, exposes these hidden relationships. By mapping associations and scoring their strength, institutions can spot suspicious clusters and prioritise investigations around high-risk networks. Advanced tools use pattern detection to rank related users by shared attributes, giving analysts a clear starting point.
This approach moves firms beyond reactive alerting – enabling them to disrupt organized syndicates before they spread.
Closing the Insight-to-Action Loop
Detection alone is no longer sufficient. The true value of horizontal intelligence lies in closing the loop – translating insights into operational defenses. Deloitte’s 2024 research shows that organisations integrating detection, decisioning and response workflows achieve significant gains in efficiency and effectiveness.
Today’s analytics engines can convert complex findings into human-readable rules or automated triggers. Analysts can describe risk scenarios in natural language, and AI generates corresponding policies or filters in real time. This allows institutions to adapt to new typologies with agility, deploying interim controls without lengthy development cycles.
Equally important outcomes, whether confirmed fraud or dismissed false positives, feed back into the system to continuously retrain models. Over time, this feedback loop sharpens precision, reduces noise and makes it harder for adversaries to exploit gaps.
The Unified Intelligence Advantage
Criminal networks operate horizontally, moving seamlessly across business lines, data sets and geographies. Institutions that remain siloed leave themselves vulnerable. Those that embrace unified risk architecture – driven by real-time data correlation, feedback loops and contextual network analysis – gain the ability to see further, act faster and contain incidents more effectively.
Horizontal intelligence is fast becoming the foundation for resilience in financial crime defense. Institutions that use AI in this way secure not only stronger defenses and sharper agility, but also a lasting competitive advantage in an unforgiving digital landscape.
