McGill Uses Tech and Modeling to Individualize Client Risk

Ashley Karg, partner, U.S. casualty at McGill and Partners, told Insurance Journal at RIMS RISKWORLD that advances in artificial intelligence, data, and analytics let brokers treat each client's risk separately. Karg said, "There's more opportunity for brokers to embrace individualizing our clients as opposed to creating them as part of a pack, or part of a herd." She added that modeling platforms built by McGill mirror the casualty underwriting process and let brokers see how underwriters will view risks prior to market reactions.

Companies in insurance are increasingly layering data, predictive models, and simulation tooling into underwriting and placement workflows. Observed patterns in similar transitions: brokers using modeling to simulate underwriter decision paths typically combine loss modeling, exposure analytics, and scenario stress tests to surface actionable levers for renewals.

Karg described that the tools have shifted renewal conversations toward front-end strategy, retention, captives, and excess-loss anticipation, rather than post-underwriting remediation. Industry observers note that increased transparency along the value chain can improve pricing negotiations and client retention by making trade-offs around price and coverage explicit.

Observers will watch adoption metrics (how many broker teams embed modeling in renewals), the degree to which underwriters accept broker-generated simulations, and whether third-party benchmarking emerges to validate broker models. If broker models gain market credibility, they could change placement timelines and the content of renewal negotiations.