User
Compliance analyst
Needs a prioritized queue, case context, and a trustworthy explanation that can be defended later.
PM Compliance Case Study · Edenred / C3Pay
Adaptive Trust: an AI Compliance Service for faster, explainable risk decisions.
C3Pay-like payroll card programs sit at a difficult intersection: high-volume employee needs, employer administration, cross-border usage, and strict compliance expectations. Adaptive Trust is a product concept for turning scattered risk signals into consistent, auditable decisions.
Problem
Compliance teams do not lack data. They lack a usable decision layer.
Signals often live across KYC records, employer changes, transaction patterns, support tickets, and policy rules. The work becomes slow when every exception requires a human to reconstruct context from scratch.
1Customer profile, many disconnected compliance signals.
3Competing needs: speed, risk control, customer fairness.
0Room for black-box decisions in regulated workflows.
90Days to validate operational value with a narrow pilot.
Solution
An AI Compliance Service that recommends, explains, and learns.
The service would not replace policy ownership. It would assemble evidence, score cases, recommend next actions, and give reviewers a clear reason trail.
Signals
- KYC and identity changes
- Payroll and employer events
- Transaction anomalies
- Support and dispute history
→
AI Compliance Service
- Risk summarization
- Policy matching
- Evidence retrieval
- Reviewer rationale
→
Decisions
- Approve low-risk cases
- Route exceptions
- Request missing documents
- Create audit trail
Buyer
Risk operations leader
Needs throughput, consistency, SLA protection, and proof that automation is governed responsibly.
Guardrail
Human control
High-impact decisions remain reviewable. The system proposes actions and records evidence, not unchecked authority.
90-Day Plan
Start narrow, prove value, then widen coverage.
01
Discovery and policy mapping
Interview compliance reviewers, map top exception types, define evidence requirements, and identify decisions that can safely be assisted.
02
Pilot queue and explanation UX
Launch a reviewer-facing queue for one or two high-volume case types with human approval and feedback capture.
03
Operational measurement
Track review time, deflection, false positives, escalation quality, analyst trust, and audit completeness before expansion.
Product judgment
AI compliance only works when the system earns trust.
My product focus would be to make every recommendation inspectable: what signal mattered, what policy applied, what evidence was missing, and what the human reviewer should do next.