Development and Assessment of a Digital Claim Processing System in Personal Insurance

Thesis Abstract

The increasing volume and complexity of personal insurance claims necessitate the development of efficient, transparent, and user-friendly claim processing systems to enhance customer satisfaction and operational efficiency. Despite the proliferation of digital technologies within the insurance sector, many organizations still rely heavily on manual claim processing methods, which are often characterized by long turnaround times, susceptibility to errors, and limited customer engagement. This study aims to develop and critically assess a comprehensive digital claim processing system tailored for personal insurance providers, with the intent of demonstrating how automation and digital integration can improve claim management processes. The specific objectives of the research are to (1) design a prototype of a digital claim processing system that incorporates automation, artificial intelligence (AI), and secure data management, (2) implement the system within a selected insurance company, and (3) evaluate its effectiveness through quantitative and qualitative measures. This evaluation encompasses processing time, accuracy, customer satisfaction, and operational cost reduction, providing a multifaceted understanding of the technological intervention's impact. Employing a mixed-methods research design, the study integrates quantitative analysis through a quasi-experimental approach to compare pre- and post-implementation metrics, and qualitative insights via thematic analysis of stakeholder interviews and user feedback. The population comprises 15 insurance agents involved in claims handling, 200 policyholders who submitted claims within a six-month period, and the IT department responsible for system deployment, totaling approximately 250 participants. A purposive sampling technique was adopted to select representative participants for interviews and surveys, while a stratified random sampling method was applied to ensure diversity across customer segments. Data collection instruments include structured questionnaires measuring customer satisfaction, system usability, and operational efficiency, alongside system logs for processing times and error rates. The validity and reliability of the questionnaires were established through expert review and Cronbach’s alpha coefficients exceeding 0.80. System performance data were extracted directly from the digital platform and verified for accuracy. The analysis involves descriptive statistics to summarize key metrics, paired t-tests and multivariate regression analysis to assess the significance of performance improvements, and thematic coding to interpret stakeholder feedback. The study anticipates that the deployed digital claim processing system will significantly reduce claim processing time by at least 30%, increase claim approval accuracy by 20%, and elevate overall customer satisfaction scores by approximately 15% compared to traditional manual methods. Cost analysis is expected to reveal a reduction in operational expenses associated with claims management. The findings are hypothesized to support the Technology Acceptance Model (TAM) and the Diffusion of Innovations Theory, indicating high perceived ease of use and relative advantage among users. This research contributes to the existing body of knowledge by providing an empirically validated framework for integrating digital technologies into personal insurance claim workflows. It elucidates the tangible benefits and potential challenges of system adoption within the insurance industry, offering actionable insights for practitioners aiming to modernize their claim processes. Additionally, it demonstrates the applicability of combining system development methodologies with evaluation strategies rooted in both technical and user-centered perspectives. The main conclusion underscores the efficacy of digital claim processing systems in transforming traditional insurance claims handling, promoting efficiency, accuracy, and customer-centric service delivery. Recommendations include scaling the system across multiple policy lines, continuous system refinement based on user feedback, and the incorporation of advanced AI capabilities such as predictive analytics for fraud detection. Future research is suggested to explore machine learning integration, cross-insurer system interoperability, and the long-term impacts of digital transformation on the insurance value chain.