payment-fraud-risk-reviewer

name	payment-fraud-risk-reviewer
description	Review payment fraud signals, velocity controls, risk rules, step-up actions, manual review, false positives, and feedback loops.
version	1.0.0
since	2026-08-27
last_modified	2026-08-27
authors	["platform-engineering"]
stability	stable
min_platform_version	{"codex":"unknown","amazon-q":"unknown","antigravity":"unknown","auggie":"unknown","bob":"unknown","claude-code":"unknown","cline":"unknown","codebuddy":"unknown","continue":"unknown","costrict":"unknown","crush":"unknown","github-copilot":"unknown","gitlab-duo":"unknown","factory":"unknown","forgecode":"unknown","opencode":"unknown","openhands":"unknown","cursor":"unknown","roo-code":"unknown","kiro":"unknown","junie":"unknown","gemini-cli":"unknown","iflow":"unknown","kilocode":"unknown","kimi":"unknown","lingma":"unknown","pi":"unknown","qoder":"unknown","qwen":"unknown","windsurf":"unknown","ollama":"unknown"}
deprecated_since	null
replaces	null
supersedes	[]
changelog	[{"version":"1.0.0","date":"2026-08-27","change":"Initial generated production-ready SDLC / DevSecOps skill"}]

Payment Fraud Risk Reviewer

Purpose

Review payment fraud controls, velocity and behavioral signals, risk rules, step-up actions, manual review, false-positive impact, account takeover, and post-transaction feedback loops. Treat regulatory, security, and operational references as review and evidence guidance, not legal advice.

Goal and behavioral contract

The authoritative Goal and artifact references are defined in descriptor.yaml. Capability boundaries, identity and delegation requirements, tool permissions, data boundaries, invariants, approval requirements, output contract, and operational limits are defined in contract.yaml. MCP/A2A trust boundaries and the reviewed execution closure live in integrations/ and dependencies.yaml; ASPS and assurance requirements live in assurance.yaml.

Treat those declarations as mandatory execution constraints. skcr validates requirements but does not claim verification or enforce them at runtime.

When to use

• payment fraud risk decisions, controls, or operating practices need independent review.
• A change affects payment fraud risk artifacts such as fraud rule set, risk score, velocity control, manual-review queue, step-up policy, chargeback feedback dataset.
• The user needs evidence-oriented findings for risks such as card testing, account takeover, friendly fraud, rule evasion, false-positive customer blocking, biased or opaque decisioning.
• Audit, security, operations, or platform stakeholders need a concise readiness position.
• Existing documentation, tickets, tests, or logs must be turned into actionable remediation items.

Operating model

1. Identify the relevant payment fraud risk artifacts, owners, systems, environments, and review boundary.
2. Compare the available artifacts against expected signals such as authorization decline trend, chargeback ratio, rule hit rate, manual-review outcome, device and account signal, false-positive measurement.
3. Separate confirmed gaps from assumptions, missing evidence, and advisory improvement opportunities.
4. Rate findings by operational, security, compliance, customer, and auditability impact.
5. Recommend minimal remediation steps, validation evidence, owners, and review cadence.
6. Model payment changes as explicit, monotonic state transitions and keep provider state, order state, fulfillment, and ledger effects independently reconcilable.
7. Use the provider's current official documentation and pinned API or SDK version as evidence; call out version-sensitive assumptions instead of relying on memory.

Spec-Driven Change Context