Automated Payroll Compliance Assurance: Linking Withholding Algorithms to Financial Statement Reliability

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@article{1711009,
      author = {Akindamola Samuel Akinola, Olaolu Samuel Adesanya, Chizoba Michael Okafor, Blessing Olajumoke Farounbi},
      title = {Automated Payroll Compliance Assurance: Linking Withholding Algorithms to Financial Statement Reliability},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {9},
      pages = {341-357},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711009.pdf},
      abstract = {Payroll systems are at the core of organizational operations, ensuring accurate employee compensation, adherence to tax obligations, and compliance with statutory regulations. However, manual and semi-automated processes often introduce errors in withholding, leading to compliance failures, financial misstatements, and reputational risks. This paper explores the development of an automated payroll compliance assurance model, linking withholding algorithms directly to financial statement reliability. Drawing exclusively from secondary literature, the study synthesizes research on payroll automation, compliance analytics, algorithmic governance, and audit assurance to propose a conceptual framework for aligning payroll processes with enterprise-wide financial integrity. The framework suggests that algorithmically assured payroll compliance enhances both internal control efficiency and external reporting accuracy, thereby mitigating risks associated with regulatory penalties and shareholder mistrust.},
      keywords = {Payroll Automation, Compliance Assurance, Withholding Algorithms, Financial Reporting, Audit Reliability, Internal Controls},
      month = {March},
  }