A governed AML intelligence platform, not another black-box point solution.
SherlockAML should be deployed as a modular Databricks capability that integrates with current AML systems, strengthens the data foundation, and gives model, agent, and evidence workflows the controls banks need.
What must be in place to make SherlockAML production-ready.
Infinitive focuses on integration, security, model governance, and operationalization — the areas where AML pilots often break when moving to real bank environments.
Data ingestion and conformance
Map source systems, land history and streams, standardize entities, preserve lineage, and build medallion layers for investigator and model consumption.
- Core banking, transaction monitoring, KYC/CDD, sanctions, case data
- Batch and streaming patterns
- Data quality rules and reconciliation
Governance and security
Use Unity Catalog patterns for access control, data lineage, masking, row-level security, workspace separation, audit logs, and policy-aligned permissions.
- PII/PCI and customer data controls
- Evidence lineage
- Role-based investigator access
Risk scoring and model lifecycle
Operationalize model development with model registry, champion/challenger patterns, inference logging, drift monitoring, feedback loops, and MRM evidence.
- MLflow governance
- Feature and inference traceability
- Analyst feedback capture
Agentic investigation layer
Configure agents for structured queries, policy retrieval, adverse media summarization, graph exploration, evidence packaging, and narrative drafting.
- RAG over policy and procedures
- Genie spaces for governed questions
- Human approval gates
Graph and entity resolution
Resolve customers, counterparties, accounts, businesses, addresses, devices, and relationships so investigators can see networks rather than isolated alerts.
- Structuring and layering patterns
- Mule networks and shared attributes
- Relationship explorer UX
Operational application integration
Deploy SherlockAML interfaces and integrate status, notes, evidence, and actions with case management, QA, SAR systems, IAM, SIEM, and reporting processes.
- Executive dashboard
- Investigator workbench
- Evidence export/API patterns
Modular deployment keeps the bank in control.
The right starting point depends on data readiness, existing AML investments, model governance maturity, and appetite for change.
Layer onto existing alerts
Start with existing transaction-monitoring output and add agent-assisted evidence, graph context, and narrative drafting while leaving core alert generation unchanged.
Build the governed data foundation
Prioritize the lakehouse layer, data quality, lineage, and access controls first when source fragmentation is the biggest blocker.
Deploy end-to-end pilot
Stand up a full SherlockAML pilot with investigator workbench, executive view, scoring, graph, policy assistant, and evidence pack workflow.
Use a readiness assessment to pick the safest path to production.
Infinitive can assess source systems, data availability, control requirements, security patterns, pilot typologies, and production deployment constraints.