Agentic AI for Financial Crime Compliance
Scale financial crime operations with less manual effort and greater control. Symphony Risk Intelligence (SRI) combines purpose-built agents, shared risk context, and embedded governance to coordinate work across your existing systems. Apply specialist expertise across investigations and risk assessments, while retaining oversight of the decisions that matter.
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Introduction
SRI’s agent-native platform puts specialist AI agents to work across onboarding, due diligence, screening, monitoring, investigation, and reporting. Shared risk context connects their work, reducing repeated analysis and manual handoffs so teams can devote more attention to complex judgments and higher-risk activity.
Start where operational pressure is greatest, using packaged agents alongside your existing technology. As you expand, a common orchestration and governance framework lets you combine agents from SymphonyAI, your own teams, and third parties. You gain the flexibility to develop proprietary capabilities and scale agent-led operations while maintaining consistent policies, approvals, and human oversight.
Challenge
AI can speed up individual tasks while leaving teams responsible for the work around them. Investigators still gather information, reconcile findings, and coordinate handoffs across systems. These demands consume specialist capacity and delay decisions, so rising volumes continue to create pressure for more people.
SymphonyAI’s FinCrime Frontier 2026–27 report found that 82.1% of surveyed organizations remain at or below the AI pilot stage. Moving beyond pilots means making AI work reliably across existing data, systems, and controls.
As adoption expands, teams must also validate performance, oversee agents from different sources, and manage processing costs. Handling these requirements separately for each use case adds to the coordination burden. Institutions risk accumulating more technology to manage without gaining the capacity they need to address financial crime.
How it works
SRI connects specialist agents to the data, tools, and policies needed to complete financial crime workflows. Manager Agents coordinate execution, with shared context and human review carried throughout the process.
A unified FinCrime ontology connects customer, transaction, relationship, and risk information across systems. Agents use this shared context to build on existing findings. System of Intelligence connects changing threat and regulatory intelligence to institutional exposure and recommended actions. Teams can reuse customer context and investigation findings across workflows.
An Investigation Manager Agent coordinates transaction analysis, entity research, and evidence gathering, selecting the next specialist based on earlier findings. Conflicting evidence can trigger further checks or human review. The Evidence Board brings together analysis, supporting sources, and a recommendation, so investigators begin with the context needed to assess the case.
Apply common permissions, policy boundaries, and approval points to your own and third-party agents alongside SymphonyAI agents. SRI supports evaluation of changes to models, prompts, and datasets, alongside performance monitoring and execution tracing. Institutions can inspect supporting evidence and retain control over what agents access, which actions they take, and when people intervene.
SymphonyAI’s domain-specific language models (DSLMs) bring specialist financial crime capability to defined tasks. SRI combines deterministic processing, DSLMs, and larger models according to the work required. Its model-agnostic architecture supports choices based on performance, cost, and data residency requirements, helping institutions scale agent execution within their technology strategy.
Outcomes
SRI changes how financial crime work progresses, reducing preparation and coordination effort while preserving the evidence behind decisions. Institutions can increase capacity, apply specialist expertise consistently, and keep controls aligned with changing risk.
Agent-led case preparation allows specialists to focus on assessing evidence and reaching conclusions. Agent-led sanctions investigations at a major US financial institution reduced manual investigative effort by 90%, with more than 300 alerts processed per hour using 50 agents in parallel. Preparing the case once also reduces repeated information gathering across review stages.
Read the agentic AI case study
Threat and Regulatory Intelligence Agents structure new intelligence, assess potential coverage gaps, and recommend control changes for testing and approval. This supports a shorter path from relevant intelligence to approved updates, with each recommendation traceable to its source and rationale. Investigation outcomes can inform subsequent assessments and proposed improvements to detection.
Coordinate high-volume research, triage, and investigation preparation without a proportional increase in manual effort. Specialist models and deterministic processing help control processing costs as automation expands, while phased adoption lets institutions target workflows where the operational value is greatest.
Define where human judgment is required and retain evidence of agent actions, recommendations, and approvals. Common governance across your agent ecosystem helps teams apply policies consistently, evaluate changes, and demonstrate how decisions were reached during internal audit and regulatory review.
Explore Responsible AI
Put agentic AI to work across your existing systems
Start with the workflow where manual effort or operational demand is greatest. SRI brings financial crime expertise, agent orchestration, and embedded governance into your existing environment, with flexibility to incorporate your own agents and models. Extend adoption as your needs evolve, building toward Always-on Compliance™ with greater capacity and control.
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