Financial ServicesAI implementation case study

Financial Services – B2B Payments Fintech: Fraud & Merchant Monitoring AI

AI agents for transaction graph analysis, merchant behavior clustering, case building, and regulatory filing assist.

This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.

Key takeaway

PrimeAxiom replaced linear fraud queues with AI-orchestrated investigation systems spanning data, narrative, and compliance output.

The engagement

From operational friction to a connected AI system

01 · Context

Business overview

Industry: payments/fintech. Complexity: AML/BSA obligations.

02 · Challenge

Core problem

Alert fatigue, slow investigations, inconsistent SAR quality.

03 · Approach

Solution

PrimeAxiom built fraud ops automation: graph-powered anomaly agents, narrative case briefs, automated evidence packets, and SAR draft agents with human attestation.

04 · Impact

Results

Alert precision +37%; analyst cases/day +2.1x; time-to-SAR −58%; fraud loss rate −24%.

System design

The full automation system

The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.

Graph Agent, Case Brief Agent, Evidence Agent, SAR Draft Agent.

Workflow

How the automation runs

1. Transaction stream. 2. AI scores risk clusters. 3. Cases instantiated. 4. Analyst reviews brief. 5. SAR draft generated. 6. Compliance attests. 7. Feedback improves graph.

Intelligence layer

AI agents and decision logic

Detection Agent, Narrative Agent, Export Agent.

Under the hood

Technical architecture

Real-time + batch, enterprise LLM with PII controls, immutable case logs.

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