Expense Category Auditor
PROAutomatically identify miscategorized expenses, duplicate submissions, policy violations, and fraud patterns using ML anomaly detection. Achieve 90-95% detection accuracy with contextual behavioral analysis.
Example Usage
“I have 6 months of expense data from our ERP export. Analyze for categorization inconsistencies, duplicate submissions, and policy violations. Our policy requires manager approval for expenses over $500 and VP approval over $5,000. Flag any vendors coded to multiple GL accounts.”
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| Detection aggressiveness (0-1). Higher catches more issues but increases false positives. | 0.75 | |
| Similarity threshold for duplicate detection (0-1). Accounts for minor amount/description variations. | 0.85 | |
| Violation handling: soft_alert (warning), hard_stop (block), or escalate (route to approver). | soft_alert | |
| Enable behavioral baseline comparison for contextual anomaly detection. | true | |
| Dollar amount triggering higher approval authority. | 5000 | |
| Require receipt image analysis for expenses above threshold. | true | |
| Enable automatic quarterly GL coding pattern audits. | true |
Research Sources
This skill was built using research from these authoritative sources:
- Expense Coding Audit Red Flags Comprehensive guide on categorization inconsistencies and audit triggers.
- Expense Fraud Detection and Prevention AI-powered fraud detection methods and prevention strategies.
- Preparing for an Expense Audit 2025 Audit preparation checklist and best practices.
- ML-Based Personal Finance Assistant Multi-model approach for expense categorization and anomaly detection.
- Expense Tracker Using Machine Learning ML techniques for expense tracking and pattern analysis.
- Graph Neural Networks for Fraud Detection Advanced GNN approaches for financial fraud detection.
- Anomaly Detection in Financial Data Contextual anomaly detection techniques for financial systems.
- Deep Learning for Financial Anomalies Deep learning methods including autoencoders and LSTM for anomaly detection.