The impact of AI investment on insurance company valuations

Will AI drive real value improvement in insurance, or widen the gap between market leaders and the rest?

The insurance market is no different to the wider financial services market – everyone is seeking to enhance performance through AI spend. While AI is rarely out of the headlines, the commercial reality is less clear. We have found ourselves asking, what does that mean for valuations in the insurance space? Will it drive real value enhancements for all, or will it stratify the market, accelerating the proposition of the best-in-class relative to others?

Where firms are investing

There are many areas that insurance firms are targeting for AI investment, although broadly these fall into three categories:

1. Underwriting efficiency/process – claims management and customer interaction where you change the way your business works to increase margin.
2. Pricing – can you price risk better using big data and a “primed” AI system, while ensuring the data feeding the systems is accurate and enhances decisions made by employees.
3. Distribution – how to get the right product to the right customer.

Productivity gains are visible, but value is harder to prove

Whilst some firms have realised productivity gains and are seeing benefits across processes where they have made investments, these gains appear most visible in operational use cases. Productivity gains are the number one reported outcome, represented in 75% of use cases*, while AI-driven transformations are already achieving 20–40% reductions in customer onboarding costs and 10–20% improvements in insurance agent productivity**. However, it is also true that these gains are yet to be fully realised across whole systems, particularly where functional integration capacity has not been achieved (i.e. integrated digitization across whole systems and processes).

Equally, productivity gains are easier to measure and monitor than the value implications of these investments (i.e. RoI or RoE impacts), and beyond that the overall longer term value impact. This is also reflected in FRP’s recent wider mid-market AI survey. While 44% of business leaders reported improved profit margins from AI and 42% said they had increased output without increasing headcount, lenders remain focused on proof. Only 5% of lenders said businesses consistently demonstrate AI’s financial impact within forecasts. The implication is that operational benefit is becoming visible, but the evidence required to support value is lacking.

Strategy and integration matter

It is becoming increasingly important when assessing value to understand the company’s strategy, including AI, as well as the business plan and market positioning. AI can enable and accelerate delivery, but it will not compensate for a lack of cohesive strategy. AI leaders are more likely to deploy AI in front-office growth use cases, 66.7% versus 35.3% of slower adopters, and rebuild core systems with embedded AI, 58.3% versus 6.5% of laggards***. This has always been critical but is more so now given the potential for rebased valuations off the back of AI investment.

Being able to contextualise the spend is vital, as differing levels of AI adoption, strategic integration and RoE will inevitably create winners and losers. It therefore becomes important to ask whether those winners are augmenting value, or creating additional value over traditional returns, and creating a multiplier on their current offering versus maintaining competitive advantage by adapting their delivery models to a changing world.

We see this manifesting in the question: how does AI spend impact value in the insurance industry, and does it cause a rebasing of expected multiples? Leading on from that, how should relative positioning be assessed, not least as the fundamentals of the insurance market are yet to change.

From productivity to value

That leads to the more practical valuation question: where does AI investment translate into financial performance, and when does it become meaningful enough to affect value? Productivity gains are one indicator, but the more important test is whether spend can shift margin, underwriting return, capital allocation, peer positioning or expected multiples.

In our second article, we consider those dynamics across the UK insurance markets, where adoption strategies, operating models and value levers differ.

Find out more about FRP’s Valuations team.

Sources
* Evident: 65 publicly disclosed AI use cases with outcomes; productivity gains represented in 75% of use cases.
** McKinsey: AI-driven transformations achieving 20–40% reductions in customer onboarding costs and 10–20% improvements in insurance agent productivity.
*** NTT Data Group: 66.7% of AI leaders deploy AI in front-office growth use cases versus 35.3% of laggards, and 58.3% of AI leaders rebuild core systems with embedded AI versus 6.5% of laggards.