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AI-Driven Insights Give Financial Crime Teams a New Edge

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Digital intelligence firm Fivecast is bringing its AI-powered platform—originally used in national security—into the financial crime space.

The platform is designed to help compliance and investigation teams better detect risk by surfacing insights from vast amounts of online, publicly available data. Built around open-source intelligence (OSINT), it applies machine learning to identify red flags across customer profiles and transactions—speeding up risk assessments and reducing manual workloads.

According to the company, the platform has shown a four-fold increase in efficiency over traditional investigative methods when applied to KYC, EDD, and AML use cases. This comes at a time when regulatory bodies are demanding more from firms: new definitions and obligations under EU AMLD6, recent enforcement activity from FinCEN and the OCC, and revised AUSTRAC guidance have raised the bar for due diligence.

Legacy systems and static data sources often fail to capture the broader online activity that may signal criminal behaviour. Fivecast’s offering aims to close that gap by analysing a broad “digital footprint” across online content, including multimedia. The goal: a more complete, risk-based view of customer behaviour and affiliations.

The approach is gaining traction amid growing enforcement. In 2024, U.S. regulators alone imposed more than $4.3 billion in fines related to financial crime, including a $3 billion penalty against TD Bank for shortcomings in due diligence processes.

“Some banks employ thousands of analysts to perform enhanced due diligence and investigate money laundering or terrorism financing,” said Duane Rivett, Co-founder and VP of Strategic Growth at Fivecast. “Just as national security agencies use our products to analyse extremist or terrorist networks online, banks are doing the same with a slightly different focus.”

While the company recently received recognition from the Australian Government’s Department of Defence for its innovation in open-source intelligence, its expansion into the financial sector reflects broader adoption of national-security-grade tools in commercial compliance. The trend suggests financial institutions are shifting from traditional workflows to more adaptive, intelligence-led approaches to risk and compliance.

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