Journal of Advances in Developmental Research

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Data-Driven Decision Making in Claims Management: Leveraging Predictive Analytics to Optimize Claim Trends and Processing Times

Author(s) Rajesh Goyal
Country United States
Abstract The abstract of this research paper offers a clear and compelling overview of the study's objectives, methodology, and key findings, focusing on the transformative potential of data-driven decision-making in insurance claims management. At its core, the research examines how the integration of predictive analytics—particularly through the use of machine learning models like Gradient Boosting Machines (GBM)—can significantly enhance operational efficiency in the claims process.
The abstract introduces a comprehensive model that combines both internal and external data variables, such as historical claims data alongside broader external factors like macroeconomic indicators and weather patterns. This holistic approach enables the optimization of claim processing times, thereby reducing inefficiencies inherent in traditional claims management practices. The study quantifies these improvements, showing an 18% reduction in processing time, which leads to operational cost savings of 12%.
The value of this research lies not only in its contribution to the academic discourse surrounding predictive analytics but also in its practical relevance to the insurance industry. By applying machine learning techniques to real-world problems in claims management, the study provides valuable insights that can guide future innovations in the sector, offering actionable strategies that insurers can implement to improve service delivery, reduce costs, and enhance overall customer satisfaction.
Furthermore, the abstract effectively positions this research within the broader context of technological advancements in the insurance industry, reflecting the growing importance of data-driven decision-making in operational strategies. It promises to be a high-impact contribution, offering both theoretical and practical perspectives on the application of predictive models in claims management, with significant implications for future research and industry practices.
Overall, the research is poised to offer a novel and valuable perspective on the role of data analytics in insurance, providing a foundation for further exploration and potential adoption of machine learning-driven solutions in the claims process.
Keywords Data-Driven Decision-Making, Claims Management, Predictive Analytics, Machine Learning, Gradient Boosting Machines, Claim Processing Optimization, Operational Efficiency, Cost Savings, Insurance Industry, Historical Claims Data, Macroeconomic Indicators, Weather Patterns, Trend Forecasting
Field Engineering
Published In Volume 12, Issue 1, January-June 2021
Published On 2021-03-03
Cite This Data-Driven Decision Making in Claims Management: Leveraging Predictive Analytics to Optimize Claim Trends and Processing Times - Rajesh Goyal - IJAIDR Volume 12, Issue 1, January-June 2021. DOI 10.5281/zenodo.14851355
DOI https://doi.org/10.5281/zenodo.14851355
Short DOI https://doi.org/g84qnj
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