Akur8: Actuarial Agility for Smarter Pricing in Fast Moving MGA Markets

The UK MGA sector has firmly established itself as the most dynamic segment of the property and casualty market, with over 300 MGAs now collectively managing more than 10% of the UK’s £47 billion in general insurance premiums (MGAA, 2024). While productivity is rising, average profitability ratings fell for the second consecutive year to 49.3%. This margin pressure is a direct threat to MGAs operating on a commission and profit contingent model.

In niche and fast-moving markets, this creates a difficult pricing challenge where teams must make decisions with limited claims history, fragmented broker data, and rating structures that are often inconsistent across the book. For MGAs looking to scale profitably, actuarial agility is no longer just about updating rates faster, it is about identifying where to grow, where margins are leaking, and how to support pricing decisions with transparent analytics that carriers can trust. (Insurance Times, 2025)

Most MGA income comes from commission, accounting for approximately 70% of an MGA’s revenue with the remaining coming from profit contingent arrangements and payments from capacity providers when the portfolio performs below an agreed loss ratio threshold. The relationship between loss ratio performance and profit commission income is non-linear. A portfolio that drifts a few points the wrong side of that threshold can result in earning nothing at all. Improving processes to ensure we stay on the right side is crucial. An improvement in model accuracy will shift the expected loss ratio. This will give higher certainty of staying within thresholds and also allow a larger amount of room to optimise the prices.

One common constraint MGA pricing teams are facing is the quality and completeness of the data they work with. Established carriers may have large homogenous claims data, while MGAs, especially in specialty books, tend to have rating variables that are inconsistent across coverholders from different brokers or underwriting systems and extracting useful information from this environment is hard.

The default response has historically been to rely more heavily on market benchmarks and manual judgement, and to build pricing models that are deliberately conservative given the data limitations. The problem is that conservative pricing in a competitive market costs volume. Lower volume reduces the commission base while doing little to protect the loss ratio threshold if the underlying risks are not well understood.

Modern actuarial platforms are specifically designed to extract more value from imperfect datasets.  By applying transparent, explainable machine learning techniques, pricing teams can identify credible rating signals even in sparse data environments, build models that are auditable and defensible to internal governance and external stakeholders, and iterate on those models far more quickly than other workflows allow. In Akur8’s pricing survey, we found 81% of insurance pricing professionals rank pricing as the most important competitive differentiator, and believe greater convergence between actuarial science and data science is essential. This is a commercial priority for MGAs right now.

Even where MGA actuarial teams have good models, many face a structural problem where the gap between identifying a pricing issue and deploying a fix is too long.

In a typical MGA workflow, the journey of making corrections based on new data, to getting those rates live in the market can stretch from to three to six months. Model rebuilding, internal sign-off, documentation preparation, and IT handover to push rates into the policy administration system all add time. And it is rarely a single bottleneck. Legacy architectures were not built for agility where every change becomes a mini-project. It is increasingly important that the speed of deployment matches the speed of innovation.

The longer that gap persists, the more policies are written on stale assumptions, and the more damage accumulates before a fix reaches the market. This is why deployment speed deserves its own place in the MGA actuarial improvement agenda. Much investment goes into building better models, but the value of those models only reaches the portfolio when rates actually change in the market. A modern, cloud-native rating engine changes that equation by connecting the entire pricing lifecycle in one place. Actuaries can build and validate models, run simulations, and IT can push tested updates to production in minutes, without breaking existing integrations or requiring maintenance windows.

MGAs face a genuine trade-off between growing the premium base, which increases base commission income, and protecting the loss ratio, which is what keeps profit contingents in play. That trade-off is often navigated by instinct and shaped by commercial relationships and historical precedent. The result is a pricing strategy that drifts, growing where it should be selective and holding back where it has room to push.

Modern pricing optimisation changes that entirely. Rather than treating growth and profitability as competing intuitions, optimisation tools make the trade-off explicit and quantifiable. By combining a technical risk model with a demand model and a defined set of business constraints, they map the efficient frontier between volume and margin: for any given target, what is the maximum premium that can be written? For any given volume ambition, what is the minimum rate action required to protect the profit commission threshold? They have precise, model-driven answers that can be stress-tested, simulated under different scenarios, and refreshed as the portfolio evolves.

For MGAs optimisation lets the actuarial team identify which segments of the book are genuinely worth growing, which are quietly eroding the contingent, and which rate changes deliver the best return on volume at the lowest risk to profitability. The commercial case is equally strong on the capacity side. Carriers and delegated authority partners are increasingly scrutinising MGA pricing sophistication as part of their governance frameworks. An MGA that can present clear, explainable pricing logic, demonstrate how decisions are being made at portfolio level, and show the modelled impact of its strategy on future loss ratios is materially better placed to retain and grow its capacity relationship. It gives the actuarial function a credible and transparent story to tell.

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