AI Payroll Exception Assistant
Payroll anomaly detection with LLM-powered plain-language explanations
Payroll exception review tool wiring LLM analysis into a structured data pipeline — demonstrates responsible AI integration, domain-aware product thinking, and compliance-first architecture.
Problem
Payroll teams manually review hundreds of exception reports each cycle, burning hours on pattern recognition that could be automated.
Goal
Create a tool that ingests payroll data, flags statistical outliers, and provides LLM-generated explanations so reviewers can act faster with higher confidence.
What I Built
A Next.js application with a structured data pipeline that feeds payroll records through anomaly detection rules — designed to surface flagged items with OpenAI-generated plain-language summaries once the LLM phase is wired in.
Key Features
- Automated exception detection across overtime, missed punches, and pay variances
- Planned: LLM-generated plain-language explanations per flagged item
- Batch review workflow with approve/escalate/note actions
- Audit trail for compliance and manager sign-off
- Dashboard summary with trend charts
Challenges
- Handling sensitive payroll data responsibly — no PII sent to external APIs
- Tuning anomaly thresholds to minimize false positives without missing real issues
- Structuring LLM prompts to produce consistent, auditable explanations
Outcome
Data model and anomaly detection rules are built and tested. Next phase wires in LLM-powered explanations with PII-safe prompt design.
Current Status
Data model and exception detection rules are complete. LLM analysis with PII-safe prompt design is the next phase.