Start where value, data readiness, and control needs intersect.
The best SherlockAML entry point is the use case that proves measurable value without forcing the bank to replace its entire AML ecosystem on day one.
Use cases that connect technical capability to AML outcomes.
False-positive triage
Prioritize existing alerts using contextual data, policy checks, transaction history, customer risk, prior outcomes, and ML risk signals.
- Outcome: reduced non-actionable case work
- Audience: AML operations and risk
- Technical path: alerts + governed data + scoring
Structuring detection
Identify patterns of smaller transactions designed to avoid thresholds by linking accounts, counterparties, branches, channels, and timing windows.
- Outcome: better network pattern visibility
- Audience: investigations and fraud strategy
- Technical path: graph + typology logic
Synthetic identity clusters
Use entity resolution and shared attributes to connect customers, addresses, phones, devices, employers, businesses, and counterparties.
- Outcome: broader risk coverage
- Audience: KYC/CDD and fraud teams
- Technical path: entity resolution + graph
SAR narrative acceleration
Generate a draft narrative, evidence packet, and source links so the investigator reviews and improves a draft instead of starting from scratch.
- Outcome: faster SAR preparation
- Audience: investigators, QA, compliance
- Technical path: RAG + evidence lineage
Policy and procedure assistant
Let investigators ask governed questions against the bank’s own AML policies, procedures, playbooks, and regulatory guidance.
- Outcome: more consistent decisions
- Audience: investigators and QA
- Technical path: governed RAG + citations
Executive AML operations cockpit
Give leaders visibility into backlog, aging, productivity, detection quality, SAR volume, scenario performance, and team bottlenecks.
- Outcome: better operating rhythm
- Audience: Head of AML, CCO, CRO
- Technical path: lakehouse dashboard + Genie
Adverse media summarization
Summarize adverse media and open-source context into investigator-ready findings with source traceability and review controls.
- Outcome: faster contextual research
- Audience: EDD and investigation teams
- Technical path: external signals + RAG controls
Model risk evidence pack
Collect model features, inference tables, monitoring, thresholds, rationale, and decisions into a repeatable package for MRM and audit.
- Outcome: stronger model governance
- Audience: MRM, audit, technology risk
- Technical path: MLflow + monitoring + lineage
Quality assurance sampling
Use risk signals and case patterns to help QA teams sample higher-risk decisions and identify inconsistency across teams.
- Outcome: better QA targeting
- Audience: QA and compliance testing
- Technical path: case analytics + dashboards
How to pick the first use case.
Infinitive recommends a pilot where baseline metrics are known, data is obtainable, investigator feedback is available, and control owners can validate the workflow.
Business value
Does the use case reduce cycle time, backlog, manual effort, quality defects, or regulatory friction?
Data readiness
Can the bank access the right history, source fields, policy documents, and case outcomes quickly enough?
Control fit
Can the workflow preserve human sign-off, evidence lineage, model governance, and auditability?
Scale path
Does the pilot create reusable data, controls, and integration patterns for additional typologies and teams?
Begin with false-positive triage or SAR narrative acceleration.
These often provide visible productivity gains while preserving existing alert engines and case management processes.