Abstract
Enhancing insurance processes when workers grapple with physical injuries necessitates a deep dive into the cognitive science facets to optimize recovery. Time series analysis emerges as an instrumental tool within this framework, offering profound insights and data-driven analysis, ultimately paving the way for a more refined and efficient insurance process. This paper uses time series analysis, a machine learning approach, to enhance insurance business processes by understanding the cognitive aspects of post-injury workers. We delve into the intertwined roles of legislative environments, administrative processes, and their impacts on recovery outcomes, gauged through psychometric measures. By distinguishing between “state” (changeable) and “trait” (constant) psychological variables, we ascertain how legislative measures influence these variables, especially under adverse impacts leading to discernible patterns in claims. Our study compares time series models across various legislative environments in Australia, examining the claims managed by multiple insurers to discern any variability due to legislation. This analysis is enriched by the data from the Navigator Support Program, which screens claimants through psychometric tests, providing insights into the effects of legislation and insurer behaviour on recovery from workplace injuries. The ultimate aim is to harness these insights to improve insurance business processes.
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McMahon, J.E., Roozegar, R., Craig, A., Cameron, I. (2024). Towards Improving Insurance Processes: A Time Series Analysis of Psychosocial Recovery After Workplace Injury Across Legislative Environments. In: Monti, F., et al. Service-Oriented Computing – ICSOC 2023 Workshops. ICSOC 2023. Lecture Notes in Computer Science, vol 14518. Springer, Singapore. https://doi.org/10.1007/978-981-97-0989-2_3
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DOI: https://doi.org/10.1007/978-981-97-0989-2_3
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