For years, financial institutions have relied on the same rules-based approach to financial crime compliance. Static thresholds trigger alerts, investigators sift through cases, and reports are filed with regulators. That model worked in a slower, simpler financial system, but it is no longer fit for purpose. Instant payments, global transaction flows, and sophisticated laundering networks have overwhelmed outdated processes.
SymphonyAI, which offers AI tools for financial crime prevention, recently delved into why the traditional compliance model is broken.
The scale of the challenge is clear. Institutions regularly face false positive rates as high as 95%, driven by blunt rules engines that cannot adapt to evolving criminal behaviour. Meanwhile, investigators spend more than 21 hours on each suspicious activity report, often clearing low-risk alerts while meaningful threats slip through. SymphonyAI’s Sensa Risk Intelligence (SRI) aims to resolve this by using AI to prioritise genuinely risky behaviour and automate repetitive investigative tasks.
Another issue is technology fragmentation. Many banks operate with disconnected legacy tools for AML, sanctions screening, fraud, and case management. This slows investigations and prevents teams from gaining a full picture of risk. SRI addresses this through a consolidated platform, giving analysts a single view across all financial crime workflows.
Compliance is also still widely seen as a cost centre rather than a strategic capability. Yet the data generated by compliance teams offers valuable insights into customer behaviour, risk exposure, and emerging typologies. With AI-driven analytics, institutions can turn these insights into commercial advantage while strengthening defences.
Slow system updates further compound the problem. Regulatory changes, new typologies, and sanctions updates often take months to implement. SRI’s agent-based architecture enables much faster deployment, reducing exposure during transition periods.
Explainability remains critical too. Regulators expect clarity on how decisions are made, yet many legacy models lack transparency. SRI includes explainable AI to produce clear, auditable reasoning.
Together, these challenges show why the traditional compliance model is breaking. AI-led platforms such as SRI offer a path forward—more accurate, more agile, and significantly more efficient.
For more insights, read the full story here.
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