Screening built to separate risk from noise
SymphonyAI combines enriched watchlist data, precise screening, and agent-led investigations to reduce false positives, lower operating costs, and strengthen regulatory confidence
Name Screening | Transaction Screening
Adverse media | PEPs | Internal Watchlists
Screening noise comes at a cost
.svg)
Precision and capacity at scale
False positive reduction
Less manual investigation effort
Faster case resolution
Supported global watchlists
Languages supported
Decision traceability on every agent action
The complete screening lifecycle
Bring customer, transaction and watchlist data into a consistent screening process. Combine external lists with internal watchlists, and assess record quality before matching. Clearer source data helps reduce avoidable noise while supporting coverage across the screening program.
- Ingest watchlists in real time or batch, with automated updates
- Normalize customer and transaction data for consistent screening
- Use NoiseRank to assess name clarity, identifiers and alias quality
Apply matching logic that reflects the customer, list and screening context. Configurable algorithms handle spelling differences and name variations, while supporting identifiers help assess match strength. Screen names and payments at scale with controls aligned to institutional policy.
- Combine weighted fuzzy matching, synonyms and multi-field checks
- Configure matching sensitivity using watchlist quality and approved thresholds
- Screen payments across Swift MT and MX, ISO 20022 and Fedwire
Reassess matches before they reach investigators. Combine rules, predictive AI and agent-led watchlist enrichment to distinguish likely false positives from potential exposure. Configure multiple scoring models independently from the policies that govern suppression, escalation and human review.
- Add cited identity, ownership and sanctions context with the Watchlist Enrichment Agent
- Use RiskRank to prioritize by potential exposure if a match is genuine
- Apply reviewer-confirmed precedent to repeat transaction matches through Decision reApplication
A Manager Agent coordinates specialized Worker Agents to gather evidence, assess screening context and prepare the case. Investigators start with findings and recommendations on a single Evidence Board, where they can challenge the analysis and make informed decisions.
- Agent-led triage automatically assesses alerts by risk, urgency and complexity, then routes them for review
- Review subject profiles, screening intelligence and web research in shared risk context
- Generate narratives and populate disclosure forms for human review and approval
Connect each screening outcome to the source data, matching logic and evidence behind it. Define where automation can act and where human approval is required. Use investigation feedback to inform controlled changes to screening policies and models.
- Trace model scores, agent actions and investigator decisions through the audit record
- Apply policy boundaries, access controls and escalation thresholds throughout workflows
- Review and govern changes before updating matching logic or automated decisions
Greater precision at every stage
Unified risk ontology
Connect customer, transaction, watchlist and relationship data through a unified FinCrime ontology. Give models, agents and investigators shared context to identify connections and assess exposure across financial crime domains.
End-to-end agent-led workflows
Reduce manual effort from alert triage through investigation and reporting. A Manager Agent coordinates specialized Worker Agents to gather evidence, route cases and prepare disclosure narratives, with human oversight and full traceability throughout.
Screening at enterprise scale
Screen high-volume payment flows in real time and extensive customer populations in batch. Elastic processing capacity scales with demand to support transaction peaks, large rescreening runs and continued growth.
Less noise, greater risk focus
Use AI to reduce false positives and repeated reviews, freeing investigators to focus on genuine risk. Lower operating costs, improve consistency and manage growing screening volumes without proportional increases in effort.
Watchlist Enrichment Agent
An AI agent researches sparse watchlist records, adding identity, ownership, sanctions and adverse media context. Cited sources and credibility assessments help resolve ambiguity and reduce manual research during match assessment and investigation.
Configurable matching
Combine fuzzy matching, synonyms and supporting identifiers. Set field-level thresholds and conditions to reflect each list, customer segment and screening requirement.
Results you can see
Evidence of screening precision, scale and reduced manual effort at global financial institutions
A major Spanish bank added AI to its existing screening environment, improving match accuracy and alert prioritization while preserving established workflows.
A major Spanish bank added AI to its existing screening environment, improving match accuracy and alert prioritization while preserving established workflows.
Read case studyA large American bank demonstrated SymphonyAI’s ability to screen ultra-high volumes, then expanded its SymphonyAI coverage to PEP and adverse media screening.
A large American bank demonstrated SymphonyAI’s ability to screen ultra-high volumes, then expanded its SymphonyAI coverage to PEP and adverse media screening.
Read case studyThe global company incorporated agentic AI into their processes to brilliant effect
The global company incorporated agentic AI into their processes to brilliant effect
Read case studyAI on your terms
Choose where AI adds value across your screening operation, with human authority built in.
Enhance your existing screening environment
Start with AI post-processing, enrichment or agent-led investigation. Expand across the lifecycle as your requirements evolve.
● Configure rules, models and agents independently at each stage
● Retain existing detection engines and case management where needed
Symphony Risk Intelligence
SRI is an agent-native risk and compliance platform purpose built to transform FinCrime
Combining unified risk intelligence with end-to-end agentic orchestration, SRI enables compliance programs to stay current by design as regulations, threats, and business activity change.
SRI is built so customers can start where they need to, expand at their own pace, and scale across regions, lines of business, and use cases without friction.
Modernize your infrastructure with a cloud-native, evergreen platform that prepares your business for the era of agentic AI and continuous AI innovation.
Our solutions are built on 25 years of proven expertise on a global scale. That’s why we’re trusted by 33% of the world’s largest financial institutions.
Related resources
Discover more about our transaction monitoring capabilities
Screening FAQs
Explore screening coverage, AI adoption and the controls that keep your institution in charge.
SymphonyAI supports watchlist management, name screening and transaction screening across sanctions, politically exposed persons (PEPs), adverse media and internal watchlists. Capabilities span data ingestion, matching, post-processing, investigation and disclosure, with real-time and batch processing to suit different workflows.
Predictive AI and contextual enrichment help distinguish likely false matches from potential exposure. NoiseRank assesses watchlist data quality, while RiskRank evaluates potential exposure if a match is genuine. Decision reApplication reduces repeat transaction screening reviews using reviewer-confirmed precedent and configured confidence thresholds.
Yes. Capabilities can operate across the full lifecycle or enhance selected stages of an existing environment, including third-party detection engines. Institutions can introduce post-processing, enrichment or agent-led investigation while retaining the systems and workflows they need.
Yes. Configure the balance of rules, predictive models and agents independently across list ingestion, detection, post-processing and investigation. Set policy boundaries, confidence thresholds and human approval requirements to reflect the workflow and risk involved.
Scoring is separate from decisioning, so a model output does not itself determine an action. Policies govern suppression, escalation and review. Audit trails connect source data, model scores, agent actions and human decisions, helping teams explain outcomes and control subsequent changes.
Focus screening effort where risk warrants it
Talk with SymphonyAI about reducing alert noise and strengthening control across your screening lifecycle.