The World’s First AI-Powered Payroll Validation
Mercans introduces Enhanced Payroll Validation with AI Insights - a groundbreaking innovation that redefines payroll accuracy, compliance, and efficiency across the globe.
Payroll Validation, Reimagined
Payroll validation has traditionally been manual, reactive, and error-prone. Errors in payroll data lead to costly corrections, compliance risks, and loss of employee trust.
Our solution combines deterministic rule engines with AI-powered anomaly detection to deliver clear, actionable insights before and after payroll processing.
Payroll validation has traditionally been manual, reactive, and error-prone. Errors in payroll data lead to costly corrections, compliance risks, and loss of employee trust.
Enhanced Payroll Validation Framework
Mercans’ Enhanced Payroll Validation operates as a dual-phase validation framework that embeds intelligence directly into the payroll lifecycle. By combining deterministic rule engines with AI-driven enrichment, the system delivers proactive detection, contextual explanations, and actionable remediation steps.
Input Validation (Pre-Payroll Stage)
This stage activates as soon as payroll input data is loaded. The system applies both rule-based anomaly detection and AI pattern recognition across 12 prior payroll cycles to benchmark current data.
Duplicate Detection
Flags multiple rows for the same pay element and date range.
Missing Elements
Identifies recurring pay elements (e.g., base salary) that suddenly disappear.
New Elements
Flags allowances, bonuses, or pay components not previously seen.
Spikes and Drops
Detects unusual percentage deviations from historical medians (default threshold: +30%).
Non-Positive Value Validation
Detects pay elements that should not logically be < 0 (except for defined exceptions like leave balances).
Instead of cryptic flags, anomalies are passed through a Large Language Model (LLM) which generates explainable summaries and corrective hints (e.g., “Basic salary missing for Employee ID 254 - verify mapping in pay element table before re-running payroll”).
Payroll Processing (Gross-to-Net)
Payroll runs as usual. During this step, the validation system runs concurrently with gross-to-net calculations, ensuring no additional latency in overall payroll cycle time.
Output Validation (Post-Payroll Stage)
Payroll runs as usual. During this step, the validation system runs concurrently with gross-to-net calculations, ensuring no additional latency in overall payroll cycle time.
Payslip Re-Creation
AI reconstructs sample payslips from gross-to-net data.
Context-Aware Compliance Validation
LLM determines country and entity context, then validates payslips against jurisdiction-specific statutory rules (e.g., tax deductions, social contributions, overtime calculations).
Sampling Strategy
The validation engine analyzes payroll output using a scalable AI-driven sampling approach, with the capability to extend coverage across hundreds or even thousands of employees as needed.
Output anomalies are returned as business-friendly compliance insights (e.g., “Tax withholding for Employee ID 382 does not align with (Country) statutory thresholds for this period. Please cross-check tax configuration settings.”).
Consolidated AI Insights
Input and output findings are aggregated into a single insight layer within the payroll summary report.
Structured Output Enforcement
All anomalies follow JSON schema formatting, ensuring consistency.
Explainability
Each anomaly is paired with “what went wrong” and “next steps.”
Feedback Loop
Dotted-line workflow enables corrections to be applied at the input stage and payroll to be re-processed with minimal delay.
Privacy & Data Handling
Only Employee IDs and anomaly data are shared with AI models
No PII (names, addresses, bank details) is processed.
Scrubbing functions automatically remove any accidental data echoes in AI-generated text.
Async batching, concurrency limits, and failover safeguards ensure reliability at scale.
