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SAR Analyzer
Turn SAR narratives into stronger detection: LLM-assisted analysis for emerging risk discovery
Consilient’s SAR Analyzer applies AI trained specifically to identify financial-crime risk patterns within SAR/STR narratives, helping teams uncover meaningful relationships, behaviors, and emerging risks across large volumes of investigative data.
The SAR Analyzer examines narrative content to identify recurring patterns and connections that can be difficult to see through individual case review. Those findings can then be translated into testable, reusable detection capabilities, helping institutions turn investigative knowledge into stronger detection while keeping sensitive narratives protected.
Key features
Analyze textual and structured SAR/STR information together to identify recurring behaviors, emerging risks, and patterns that may be difficult to see through individual case review. Clustering brings related reports together based on both narrative and non-narrative features, helping investigators and compliance leaders understand what is appearing across the wider SAR population.
Create, refine, and analyze groups of SARs to investigate specific questions or suspected behaviors. AI can review the full SAR population against targeted queries, helping uncover patterns that may not exist in structured fields alone, including unusual or previously unseen relationships between activity, entities, and circumstances.
Extract and resolve people, organizations, accounts, and counterparties across multiple SARs. The SAR Analyzer identifies when the same or related entities appear in different reports, helping investigators connect information that may otherwise remain fragmented across individual cases.
Map relationships between SAR subjects and related parties to reveal wider networks of activity. Network analysis highlights connections across investigations, while integrated sanctions screening can check entities and counterparties against rich OpenSanctions sources, adding external context to internal investigative findings.
Convert patterns discovered within SAR narratives into synthetic financial-crime scenarios and model-ready data. This allows investigative knowledge to move beyond individual reports and support the development, testing, and refinement of detection models for behaviors that might otherwise remain hidden within narrative data.
Use the AI environment that fits your institution while retaining visibility over how models are being used. The SAR Analyzer supports AWS Bedrock, locally deployed models, and leading APIs including OpenAI and Anthropic, with visibility and traceability of LLM usage to support governance, auditability, and institutional control.
Key benefits
Discover risk patterns
Identify recurring behaviors, relationships, and emerging risk patterns across large volumes of SAR/STR narratives. AI trained specifically on financial-crime narratives can connect information across cases that may be difficult to identify through individual review, helping teams surface new risks and areas for deeper investigation.
Build reusable detection
Translate patterns identified within SAR/STR narratives into testable, reusable detection capabilities. This helps institutions move valuable knowledge out of individual case files and into detection approaches that can be tested, refined, and applied more broadly.
Analyze narratives securely
Apply advanced AI analysis while keeping sensitive SAR/STR information protected within the institution's environment. Teams can extract greater value from narrative data without creating unnecessary movement or exposure of highly sensitive investigative information.
Discover risk patterns
Identify recurring behaviors, relationships, and emerging risk patterns across large volumes of SAR/STR narratives. AI trained specifically on financial-crime narratives can connect information across cases that may be difficult to identify through individual review, helping teams surface new risks and areas for deeper investigation.
Build reusable detection
Translate patterns identified within SAR/STR narratives into testable, reusable detection capabilities. This helps institutions move valuable knowledge out of individual case files and into detection approaches that can be tested, refined, and applied more broadly.
Analyze narratives securely
Apply advanced AI analysis while keeping sensitive SAR/STR information protected within the institution's environment. Teams can extract greater value from narrative data without creating unnecessary movement or exposure of highly sensitive investigative information.
Turn investigative knowledge into stronger detection
SAR/STR narratives contain valuable insight into the risks investigators are actually seeing, but that knowledge is often dispersed across thousands of individual cases. Consilient’s SAR Analyzer brings those findings together, helping financial-crime teams identify recurring behaviors, emerging risks, and connections that may otherwise remain buried in narrative data.
By converting those insights into clear reporting and testable detection opportunities, institutions can better understand how risk is evolving and strengthen what happens next. The result is a more connected approach in which investigative outcomes inform future detection, helping teams learn systematically from the cases they have already investigated.