GenAI For Fusion Payroll Cutover Testing: Automated Summarised Outputs

The difficulty of payroll cutover testing stems from the complexity of payroll data. Public sector payrolls often involve a mix of salaried and hourly staff, union agreements, allowances, deductions, benefits, and varying tax rules. This complexity is amplified when the workforce spans multiple departments, job types, and geographic locations.

In higher education, for instance, a single payroll cycle might include academic contracts, research grants, student workers, and international staff with unique tax compliance rules. In healthcare, there are additional challenges such as night shift allowances, hazard pay, and emergency cover rates.

Without automation, payroll teams spend countless hours on manual checks—identifying differences is one thing, but explaining and resolving them is often the harder task. Discrepancy analysis requires understanding whether differences stem from configuration changes, data migration issues, or genuine policy differences between old and new systems.

GenAI automates much of the process, significantly reducing the time and effort involved while improving accuracy. The technology works by ingesting payroll results from both systems, mapping equivalent fields, and comparing them at the most granular level. It can then categorise discrepancies, suggest potential causes, and generate clear, concise summaries tailored for different audiences—detailed reports for payroll teams and executive-level summaries for leadership.

For example, if an overtime payment shows a mismatch, GenAI can detect whether it’s due to rounding differences, a missing element mapping, or an actual configuration error. It doesn’t just flag the issue—it explains it. This level of insight accelerates troubleshooting and resolution.

Benefits for public sector payroll teams include:

• Dramatically reduced review time, enabling faster sign-off.
• Improved accuracy by eliminating human oversight in initial comparisons.
• Consistent categorisation of discrepancies for easier resolution.
• Clear impact assessments to prioritise fixes.
• Role-based summaries so that executives see high-level insights while payroll teams see detailed data.

The Firstcron GenAI approach follows a structured sequence:

1. Data Extraction – Payroll outputs are exported from both the legacy system and Oracle Fusion Payroll.

2. Data Mapping – Employee IDs, payroll elements, and pay periods are aligned.

3. AI Comparison – GenAI compares all mapped fields, flagging discrepancies based on configured tolerance levels.

4. Categorisation – Discrepancies are grouped into earnings, deductions, benefits, taxes, and other categories.

5. Root Cause Suggestions – AI provides probable reasons for each discrepancy.

6. Impact Summaries – Executive-friendly summaries outline overall accuracy, total financial variance, and risk areas.

7. Resolution Support – Detailed variance files help payroll teams quickly resolve issues before re-testing.

Local Government – Payroll for council workers often includes seasonal staff, public safety roles, and various allowance structures. GenAI ensures accurate cutover by automatically applying different policy contexts in comparisons.

Higher Education – With diverse employment types and funding sources, universities benefit from AI-driven categorisation to separate grant-funded payroll from institutional payroll for validation.

Healthcare – Complex shift patterns and compliance with healthcare labour laws mean discrepancies can have legal implications. GenAI provides the audit-ready documentation needed for compliance checks.

Public Sector Agencies – Agencies operating under strict budgetary oversight get clear variance breakdowns, ensuring transparency in financial transitions.

A large US healthcare provider migrated payroll for 25,000 employees to Oracle Fusion Payroll. Using Firstcron’s GenAI testing solution:

• The variance review time dropped by 80%.
• 95% of discrepancies were identified and resolved in the first cycle.
• Executive leadership received a two-page impact summary instead of a 300-page variance spreadsheet.

This allowed payroll to go live on time without post-implementation payroll disruptions.

For public sector, higher education, local government, and healthcare organisations, payroll cutover testing is a critical step in Oracle Fusion Payroll migrations. Traditional methods are slow, labour-intensive, and risky. Firstcron’s GenAI-powered approach transforms the process—automating comparisons, highlighting discrepancies, suggesting root causes, and summarising the impact for faster resolution.

The result is a faster, more accurate, and more confident payroll go-live. In a world where public trust, compliance, and efficiency matter, this is not just a technological improvement—it’s a strategic advantage.