Fraud Prevention Guide

PassAudited by ClawScan on May 13, 2026.

Overview

This is an instruction-only fraud-prevention guide with no code or credentials, but it discusses sensitive customer telemetry and automated order-blocking rules that users should implement carefully.

This appears safe to use as a planning guide. Treat its outputs as recommendations, not automatic production rules: validate thresholds, keep human review for borderline cases, and make sure any device fingerprinting, behavioral tracking, chargeback evidence, or identity verification program complies with PCI, privacy laws, customer notices, and retention limits.

Findings (2)

Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.

What this means

Poorly tuned fraud rules could reject legitimate customers or reduce sales.

Why it was flagged

The guide includes automated decline outcomes that could block real ecommerce orders if the user implements them directly. This is expected for a fraud-prevention framework, but it is a high-impact business action.

Skill content
| 71–100 | Auto-decline | Decline transaction with generic error message |
Recommendation

Pilot rules on historical data, keep manual review and appeal paths, monitor false positives, and use rollback procedures before deploying automatic blocks.

What this means

Customer privacy could be affected if detailed fraud evidence is over-collected, retained too long, or shared with the agent unnecessarily.

Why it was flagged

The guide recommends collecting and retaining detailed customer, device, behavioral, account, and transaction evidence. This is purpose-aligned for chargeback response, but it creates sensitive records that require privacy controls if used as agent context or stored in a fraud system.

Skill content
Device data: Browser fingerprint... Network data: IP address... Behavioral data: Session duration... mouse movement patterns... Account data: Account age, purchase history...
Recommendation

Minimize data collection, avoid sharing raw customer records unless necessary, follow PCI/privacy requirements, restrict access, and define retention and deletion policies.