Agentic Automation
Secure Collaboration
Agentic Automation
Transforming fraud systems from reactive monitoring to proactive, self-improving
defensegrowth and success.
Dark Web Data
Secure Collaboration
Agentic Automation
Shifting from fraud strategy from reactive response to proactive exposure
detection
Secure Collaboration
Secure Collaboration
Secure Collaboration
Turning fraud defense into a shared ecosystem, rather than each institution
fighting alone
What's at Stake
Massive Fales Positive Rates
Massive Fales Positive Rates
Massive Fales Positive Rates
Mid-tier banks and credit unions often rely on legacy, rules-based fraud and AML systems that generate very high false positive rates; frequently above 90% of alerts because they lack rich, contextual data to distinguish normal from suspicious behavior. Limited access to large, diverse data sets and fragmented internal data mean these in
Mid-tier banks and credit unions often rely on legacy, rules-based fraud and AML systems that generate very high false positive rates; frequently above 90% of alerts because they lack rich, contextual data to distinguish normal from suspicious behavior. Limited access to large, diverse data sets and fragmented internal data mean these institutions must tighten rules to stay safe, which over flags legitimate transactions and overwhelms small compliance teams with manual reviews. This alert overload drives up operating costs, slows investigations, and can still miss real risk, leaving mid-tier institutions with both higher fraud losses and higher compliance burdens than data-rich global banks.
$40B+ Annual Loss
Massive Fales Positive Rates
Massive Fales Positive Rates
Fraud and AML failures are devastating financial institutions globally, with costs escalating year over year and its expected to grow by 30% annually. Mid-tier banks face disproportionate impacts, lacking the resources of enterprise competitors. As a result, they are losing 9 to 10% of their total operating budgets combating fraud and the resulting losses.
Legacy Systems Failing
Massive Fales Positive Rates
Legacy Systems Failing
Legacy platforms can’t ingest or analyze data in real time, so they miss cross-channel patterns and only catch fraud after losses and reputational damage have already occurred. Their fragmented architectures keep KYC, transaction, device, and behavioral signals in separate silos, which drives extremely high false positive rates and force
Legacy platforms can’t ingest or analyze data in real time, so they miss cross-channel patterns and only catch fraud after losses and reputational damage have already occurred. Their fragmented architectures keep KYC, transaction, device, and behavioral signals in separate silos, which drives extremely high false positive rates and forces small fraud teams to review noise instead of true risk. For mid-tier banks and credit unions, limited budgets and access to advanced tools lock them into these outdated systems, leaving them structurally disadvantaged versus larger institutions that can afford modern, data-rich AI defenses .