Reduce alert fatigue without weakening control.
Infinitive positions SherlockAML as a practical way to improve AML program effectiveness: analysts spend less time gathering evidence and more time applying judgment to the cases that matter.
Most AML programs are funded like mission-critical controls but operate like manual integration shops.
“The analyst becomes the integration layer — pulling data from siloed systems, checking policy, building narratives, and documenting decisions under regulatory time pressure.”
Infinitive market positioning for AML operations leadersWhy SherlockAML should matter to the business.
The value story is not “use AI.” The value story is better case throughput, better evidence, better governance, and a better operating model.
Collapse manual evidence gathering
Connect case evidence, customer data, transaction history, graph relationships, policies, and prior outcomes so investigators begin with a complete evidence view.
Improve false-positive triage
Pair existing detection logic with ML risk signals and agent-assembled context to help teams separate non-actionable alerts from higher-risk cases.
Accelerate SAR narratives
Generate regulator-ready draft narratives and evidence packages for investigator review, QA, and final sign-off instead of starting from a blank document.
Strengthen exam readiness
Create repeatable evidence trails with lineage, timestamps, decision notes, model context, and policy references that support defensible responses to examiners.
Protect human accountability
Keep the investigator, QA reviewer, and compliance leader in the decision path. Agents support the work; they do not replace sign-off.
Create operating visibility
Give leadership visibility into backlog, aging, throughput, alert quality, escalation patterns, analyst workload, and control effectiveness.
Executive narrative by stakeholder.
| Stakeholder | What they care about | How Infinitive + SherlockAML speaks to it |
|---|---|---|
| Chief Compliance Officer | Program effectiveness, auditability, defensible control design. | Governed evidence trails, policy-backed narratives, human approval gates, and measurable control performance. |
| Head of AML Operations | Alert backlog, analyst productivity, consistency, staffing pressure. | Agent-assisted evidence gathering, prioritization, repeatable workflows, and operational dashboards. |
| Chief Risk Officer | Risk coverage, emerging typologies, concentration risk, board reporting. | ML risk scoring, graph link analysis, scenario visibility, and executive-level trend reporting. |
| Chief Data / Technology Officer | Modernization without rip-and-replace, governance, reuse, platform scale. | Modular deployment on Databricks, integration with existing systems, and foundation for broader financial-crime analytics. |
| Finance / Transformation | Cost takeout, productivity, investment visibility, business case. | Pilot metrics tied to case cycle time, false positives, backlog burn-down, and SAR drafting effort. |
Business transformation is where AML technology either succeeds or stalls.
Infinitive brings the adoption lens: redesigned workflows, control mapping, operating model alignment, stakeholder enablement, and measurable outcomes.
Program framing
Define the business case, target outcomes, pilot scope, and executive story.
Control alignment
Map agent behavior and model outputs to risk, QA, audit, and regulatory expectations.
Workflow redesign
Redesign alert triage, evidence review, escalation, SAR drafting, and QA processes.
Change adoption
Train investigators and leaders so adoption is practical, measurable, and defensible.
Build the business case in a focused AML value workshop.
Use real operating metrics, representative alert types, existing systems, and stakeholder goals to select the best SherlockAML pilot path.