A conceptual governance framework for managing robotic process automation implementation challenges in insurance claims handling

Abstract

Background: Although insurers increasingly adopt Robotic Process Automation (RPA) to improve efficiency and accuracy in insurance claims handling, implementation projects may fail when information flows, data practices, technical capabilities and operational processes are poorly governed (hereinafter challenges). These challenges can create misalignment between business and information technology (IT) objectives and limit the sustained value of RPA initiatives.
Objectives: Building on prior research that identified key RPA implementation challenges, this study developed a theoretically informed governance framework to assist insurers in governing and managing RPA within the insurance claims-handling process.
Method: This study adopted a qualitative, non-empirical research design informed by a structured literature review. Governance guidelines were developed based on insights from the literature and mapped to the Control Objectives for Information and Related Technologies 2019 (COBIT 2019) governance and management objectives to ensure completeness and depth. The COBIT 2019 framework was used as a structuring mechanism to operationalise theoretical principles into actionable governance practices.
Results: The resulting conceptual framework integrates theoretical lenses from IT governance, data governance and IT capability theory to address the challenges of RPA implementation. The framework provides a structured tool for identifying and managing RPA-related challenges in insurance claims handling.
Conclusion: The study demonstrates that effective RPA implementation requires governance mechanisms that extend beyond strategic considerations to address detailed technical and operational challenges.
Contribution: This study contributes to the literature by offering a theoretically grounded governance framework for managing RPA in insurance claims handling, supporting improved alignment, accountability and control over automation initiatives in information-intensive processes.