Risk & Analysis
RGA Emerging-Risk Monitoring Practicum
Partnered with RGA (global reinsurance leader) on a WashU Olin client-facing practicum to develop an AI-enabled framework for identifying and monitoring emerging business and insurance risks. Delivered a structured risk intelligence framework and findings presentation to senior RGA stakeholders.
The problem
RGA needed a scalable method to identify emerging risk signals — such as novel liability exposures, technological disruptions, and geopolitical shifts — before they materialize into actuarial losses.
The approach
Designed a structured monitoring methodology: first defined what counts as a meaningful emerging risk signal (novelty, trajectory, proximity to actuarial relevance), then built an AI-assisted pipeline scanning academic preprints, regulatory filings, litigation trends, and news. Applied structured probability forecasting to score signal strength. Synthesised findings into a risk taxonomy with prioritised categories for underwriting consideration. Presented conclusions to senior RGA stakeholders.
The outcome
Delivered three artefacts to RGA: (1) a risk signal taxonomy categorising emerging exposures by type, trajectory, and actuarial proximity; (2) a monitoring methodology brief explaining how to operationalise the pipeline; (3) an executive presentation with prioritised risk categories and recommended next steps for underwriting consideration.
Business value
Supports earlier identification of emerging risk signals before they appear in traditional actuarial data — enabling more proactive underwriting decisions in fast-moving risk categories.