ReddRadar
Agent skill
fraud-risk-assessor
Fraud risk assessment skill based on the fraud triangle with red flag detection
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/finance-accounting/skills/fraud-risk-assessor
Metadata
Additional technical details for this skill
- domain
- business
- category
- audit
- priority
- medium
- specialization
- finance-accounting
SKILL.md
Fraud Risk Assessor
Overview
The Fraud Risk Assessor skill provides comprehensive fraud risk assessment capabilities based on the fraud triangle framework. It enables red flag identification, data analytics for anomaly detection, and systematic fraud risk evaluation.
Capabilities
Fraud Triangle Analysis
- Pressure/incentive identification
- Opportunity assessment
- Rationalization indicators
- Risk factor documentation
- Industry-specific considerations
- Management override risk
Red Flag Identification
- Financial statement red flags
- Behavioral indicators
- Operational anomalies
- Control environment weaknesses
- Related party concerns
- Going concern indicators
Data Analytics for Anomaly Detection
- Transaction pattern analysis
- Unusual relationship identification
- Trend deviation detection
- Outlier identification
- Clustering analysis
- Network analysis
Benford's Law Analysis
- First digit analysis
- Second digit testing
- First-two digits examination
- Conformity testing
- Deviation significance
- Result interpretation
Duplicate Detection
- Exact match identification
- Fuzzy matching
- Near-duplicate detection
- Vendor duplicate analysis
- Payment duplicate screening
- Employee duplicate review
Journal Entry Testing
- Manual entry identification
- Non-standard timing
- Round number analysis
- Unusual account combinations
- User behavior analysis
- Close period entries
Usage
Fraud Risk Assessment
Input: Entity information, industry factors, control environment
Process: Evaluate fraud triangle factors, identify red flags
Output: Fraud risk assessment report, high-risk areas, testing focus
Forensic Data Analysis
Input: Transaction data, population characteristics, risk areas
Process: Apply analytics, identify anomalies, investigate exceptions
Output: Anomaly report, investigation recommendations, findings
Integration
Used By Processes
- Fraud Risk Assessment and Investigation
- Internal Audit Planning and Execution
- SOX Compliance and Testing
Tools and Libraries
- Forensic analytics tools
- Benford's law libraries
- Data analytics platforms
- Investigation management tools
Best Practices
- Maintain confidentiality throughout assessment
- Document all red flags with supporting evidence
- Consider industry-specific fraud schemes
- Update risk assessment for changes
- Coordinate with legal counsel as appropriate
- Preserve evidence chain of custody
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