Streaming Loss Reserve Estimation and Emerging Risk Signal Detection: Real-Time AI Frameworks for Dynamic Insurance Risk Monitoring

Authors

  • Michel Beauregard Associate Professor of Geomatics Engineering, Université Laval Author

Keywords:

streaming loss reserve estimation, emerging risk signal detection, real-time ai frameworks, dynamic insurance risk monitoring, machine learning

Abstract

The insurance industry is facing major disruption with the rapid advance of technologies and rapidly changing customer expectations. New digital-native companies are entering the market and challenging traditional business models, causing an increasing need for innovation in the industry. New data sources, often unstructured and evolving at a rapid pace, are more broadly available now compared to the past. This could be from IoT sensors, social media, digital footprints, etc.

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Published

31-10-2025

How to Cite

“Streaming Loss Reserve Estimation and Emerging Risk Signal Detection: Real-Time AI Frameworks for Dynamic Insurance Risk Monitoring”. Journal of Science & Technology, vol. 6, no. 5, Oct. 2025, pp. 36-48, https://www.thesciencebrigade.com/jst/article/view/709.

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