Leveraging Accounting Analytics to Enhance Payroll Accuracy and Fraud Detection in U.S. Public Sector Institutions: A Case Study Approach

This study explores how accounting analytics can be leveraged to enhance payroll accuracy and improve fraud detection in U.S. public-sector institutions, addressing persistent irregularities amid rising demands for fiscal transparency. The research employs a qualitative design with secondary sources including academic literature, reports, and case studies. The literature identifies successful analytics implementation, such as Treasury OPI’s machine learning for data integration for unemployment claims. These precedents demonstrate direct transferability to payroll’s high volume and rules-based structure. Findings show that analytics significantly reduce improper payments through real-time screening, data integration, and risk prioritization when embedded in workflows. The findings also show that the effectiveness of analytics depends on institutional conditions such as data quality, legacy systems, technical capacity, governance, and resources. Payroll shares core risks: ghost employees, overtime abuse, and disbursement systems, therefore positioning it as well-suited for similar accounting analytical interactions. The research proposes a set of practical recommendations for public sector institutions, including data integration, prepayment screening, and capacity building in analytical skills. The study concludes that accounting analytics offers a credible opportunity for proactive oversight, but requires a phased, institution-ready implementation strategy supported by strong governance.