Articles - Insurance Pricing in Motion

By Adrian Mincher, Head of UK, Ireland and South Africa, Earnix

This is not a new problem, but it has become more acute. What has changed is the speed and nature of loss trend movement. Recent disruption has not primarily manifested as large, isolated insured events, but through second-order effects, energy costs, supply chain friction, labour inflation, that feed directly into claims. These are diffuse, persistent, and difficult to capture with traditional approaches to trend selection.

For actuaries, the challenge is less about identifying that trends are moving, and more about how quickly those movements can be reflected in pricing.

On its own, this might suggest some short-term relief in loss costs. But that view does not hold when severity is considered. Repair costs have been rising at a materially faster pace, driven by a combination of factors: more complex vehicle technology, global sourcing of parts, higher input costs for materials and energy, and ongoing pressure on labour. Delays in sourcing components are also extending repair times, increasing both direct and indirect claim costs.

The outcome is a clear divergence. Frequency may soften, but severity is increasing at a rate that more than offsets it. For pricing actuaries, this creates a more complex calibration problem. Trend selection can no longer rely on stable relationships between components of the loss ratio. It requires a more granular, and more frequently updated, view.

Construction cost inflation has been elevated for some time, but recent pressures have intensified it. Material costs, steel, cement, timber, remain sensitive to global supply conditions and energy prices. Labour markets in many regions are tight. Supply chain disruption continues to affect both cost and timing of repairs.

What complicates matters is that “inflation” is not a single input into actuarial models. Building costs, contents costs, labour rates, and logistics all move differently. Additional living expenses are influenced not just by repair duration, but by broader cost-of-living pressures.

Treating inflation as a uniform assumption risks masking these dynamics. More granular approaches, separating building and contents, incorporating regional variation, and explicitly modelling repair time inflation, are becoming increasingly necessary.

There are also emerging behavioural effects. Households under financial strain may defer maintenance, increasing the likelihood of certain types of claims. Changes in heating or energy usage can alter risk profiles in less visible ways. These are not easily captured in historical data, but they are relevant to forward-looking assumptions.

That assumption is now under strain. When key drivers of loss cost, fuel, materials, labour, can shift meaningfully within months, waiting for the next formal review cycle introduces lag. By the time assumptions are updated, the underlying environment may already have moved again.

This does not imply that pricing should become reactive in an uncontrolled way. Governance remains critical, particularly in regulated markets. But it does suggest that the frequency of monitoring, and the ability to translate updated insights into pricing decisions, needs to increase.

Leading indicators become more important in this context. Fuel prices, commodity indices, wage data, and supply chain metrics can provide earlier signals of trend movement than claims data alone. The challenge is integrating these signals in a way that is both analytically robust and operationally usable.

Scenario analysis is not new to actuarial work, but it often sits somewhat separately from core pricing processes. In the current environment, that separation is becoming harder to justify. Pricing assumptions increasingly need to reflect a range of plausible futures, not just a single expected path. What happens if inflation moderates within 12 months? What if it remains elevated across multiple underwriting cycles? How do these scenarios affect not just pricing, but reserving and capital requirements?

For longer-tail business in particular, the compounding effect of sustained inflation can be significant. Ignoring that risk, or treating it as a sensitivity rather than a central consideration, can lead to material mispricing.

This is where technology is becoming central to actuarial effectiveness. In many organisations, the gap is not a lack of models or data, but the fragmentation between them. Assumptions are updated in one environment, validated in another, and deployed through a separate pricing infrastructure. Each step introduces delay. In a stable environment, that delay is tolerable. In a volatile one, it becomes material.

The same applies to the use of external signals. While actuaries increasingly recognise the  value of leading indicators - fuel prices, commodity indices, labour costs - these are often monitored outside core pricing workflows, rather than embedded within them. As a result, insight does not translate cleanly into action.

This is where advances in analytics and AI are beginning to change the shape of actuarial work. AI is not replacing actuarial judgement, nor is it solving the problem of uncertainty. Its value lies elsewhere: in identifying patterns earlier, surfacing shifts in data that may not yet be fully credible, and enabling faster iteration of scenarios. Machine learning techniques can complement traditional actuarial approaches by detecting non-linear relationships and emerging trends across large, fragmented datasets.

Equally important is the operational layer. Modern pricing platforms are reducing the friction between insight and execution - allowing actuaries to test, validate, and deploy changes more quickly, while maintaining appropriate governance controls. The challenge is not model sophistication; it is governed deployment.

This is particularly visible in long-duration products such as annuities, where pricing precision is often high, but implementation cycles remain slow. In these contexts, the ability to apply intelligence consistently - and repeatedly - within a controlled framework is as important as the underlying assumptions themselves.

Across both general insurance and life, the competitive edge is shifting. It is less about who has access to more data, and more about who can operationalise that data faster.

Second, greater use of disaggregated data and external indicators to understand what is driving loss costs, rather than relying solely on historical averages. Third, a more explicit integration of scenario thinking into pricing, particularly where uncertainty around key drivers is high.

And finally, a focus on the operational side of actuarial work, how insights are turned into decisions, and how quickly those decisions can be implemented. None of this removes the need for judgement. If anything, it increases it. Data alone will not resolve the ambiguity inherent in the current environment. But the framework within which that judgement is applied needs to evolve.

Volatility is a structural feature of the global landscape. The actuarial response to it cannot rely on static assumptions and infrequent adjustment. Pricing, in effect, needs to be in motion. Ultimately, the goal is not just to react to volatility, but to build the capability to continuously adapt as conditions evolve. That is as much an operational and technological challenge as it is an actuarial one, in a world that is not standing still.