Modern AML investigations, built for speed, governance, and human judgment.
Infinitive helps financial institutions move beyond alert backlogs and fragmented evidence gathering by implementing SherlockAML on the Databricks Data Intelligence Platform with the operating model, controls, adoption plan, and technical delivery needed to scale.
One AML transformation story, two executive entry points.
The buyer conversation and the platform conversation need to connect. This experience gives AML executives and technology leaders a clear path to why SherlockAML, why Databricks, and why Infinitive.
Business path
Show how SherlockAML can reduce operational drag, improve examiner readiness, accelerate SAR evidence creation, and keep investigators in control of final decisions.
Go to business pathTechnical path
Explain the governed lakehouse, Unity Catalog, data pipelines, ML risk scoring, agentic reasoning, applications, and integration patterns required to productionize SherlockAML.
Go to technical pathTurn AML from manual evidence assembly into governed investigation intelligence.
SherlockAML is positioned as an agentic investigation workspace on the Databricks Data Intelligence Platform. Infinitive’s role is to turn that capability into a defensible operating model that can work inside a bank.
Unify the investigation data layer
Bring KYC, transactions, alerts, sanctions, case history, policy, and external signals together in a governed lakehouse that supports lineage, quality, masking, and role-based access.
Augment rules with richer risk signals
Use ML scoring, graph analytics, and typology detection to focus analyst attention on riskier alerts while preserving explainability and model governance.
Keep humans in the loop
AI agents gather evidence, analyze patterns, and draft narratives, but investigators, QA, and compliance leaders retain judgment, sign-off, and accountability.
Start with a focused workshop, then prove value with a production-minded pilot.
Infinitive can help scope the right entry point: evidence automation on top of existing alerts, governed data foundation, graph-enabled investigation, SAR drafting acceleration, or an end-to-end SherlockAML pilot.