Ask a dozen people in the insurance industry what is meant by ‘portfolio management’, and you’ll probably receive a dozen different responses. Dig a little deeper by asking whether they operate an effective portfolio management process, and what that looks like, and you’ll find a stark contrast in responses.
MGAs have been managing their underwriting performance since the first MGA was established in the US in the late 19th century. But managing performance and portfolio management are two different things; the former tends to be built around reporting a snapshot, retrospective position which may go on to inform prospective decisions. But reporting is reactive. It tells you what happened, not what’s coming.
Portfolio management, on the other hand, is proactive. It enables MGAs to steer profitability with precision and confidence. Steering is what takes portfolio management from static, retrospective reports to a repeatable, proactive process that blends historical understanding with forward‑looking insight, continuous tracking, with frequent, targeted adjustments.
Assess the historical performance of the portfolio at a granular, component level. Evaluate how the rating and underwriting mix changes applied in the past have impacted performance. Review and validate modelling assumptions, including robust IBNR provisions. These are the foundations on which we lay the credibility of our forward-looking outlook.
From the insights garnered through stage 1, form an actuarially informed view of how the business being written today is expected to earn out over the current underwriting period. Effectively, understand the rate adequacy of the portfolio by component.
Use the results so far to decide where to grow, maintain, or improve. Define resulting actions that are measurable so you can quantify their impact on GWP, loss ratio, and overall portfolio shape.
As results are earned through, it is fundamental to separate the signal from the noise and look for early divergence from expectations. Incorporate this with the impact of external drivers, e.g. inflation, competitor activity, and adjust quickly to keep the portfolio on track.
A good portfolio management approach will cycle through the steering loop repeatedly, enabling timely and targeted actions to be taken to meet profitability objectives.
Quite often we see that profitability is assessed in the here and now – a single snapshot of what performance looks like today, usually at a high level. There is little insight into the good (and bad) performing areas of a portfolio, and frequently, we see ‘blanket’ actions followed by a year of waiting to see if they worked.
Steering, through disciplined portfolio management, unlocks valuable insight that simple snapshots miss; it provides us with the clarity to understand:
Let’s say we have a portfolio, currently running at a loss ratio of 48% against a maximum target of 50% at which point this becomes unprofitable for a carrier.
Assuming the carrier is comfortable continuing to write to a 48% loss ratio, this leaves little margin for deterioration in performance and so early identification of any slippage, and swift corrective action is critical.
Steering makes it possible to manage the loss ratio with some certainty; it enables the portfolio to be assessed at a component part and in aggregate, it links actions to expected improvements, and it monitors whether those improvements are actually earning through.
Portfolio management enables us to answer the fundamental questions needed to achieve target performance.
To steer a portfolio effectively and achieve sustainable results, great portfolio management discipline must reflect a continuous cycle of analyse, project, and adjust. Fundamentally, at its core, it should exhibit the following characteristics:
Steering weaves together past, present, and future. It allows us to learn from history, set an informed forward view, act intentionally, and verify that reality is tracking the plan; adjusting fast when it isn’t. That’s what great portfolio management looks like for an MGA and why it should consistently deliver target profitability.
There has been a lot of speculation recently around the use of AI in insurance and how this signals the death knell for Underwriters. Having mopped up the coffee that I spat out and checked my life insurance provisions, this got me to thinking. Surely not all underwriters? But if not, then who?
The ineffable question of the “effs”. What should a business aim for – and more specifically, for us, the underwriting function in an insurance business – efficient operations or effective decision making?
We have all heard of the boiling frog syndrome which says: if a frog is put in boiling water, it will jump out to protect itself. However, if you place a frog in cold water and slowly turn up the heat, it won’t notice the subtle increase in temperature and be boiled alive.
Game theory aims to shed light on outcomes where multiple actors operate. Dry academe often assumes “ceteris paribus” even in social sciences, but in the real world when we are trying to anticipate the outcomes of decisions one actor makes it is essential to consider the possible and likely responses of other actors…
In various parts of our lives, we are examined and tested to grade our ability and knowledge, be that driving tests, ‘A’ levels, or piano exams.
And so it is with the insurance industry. Boards and regulators are increasingly pushing for demonstrable oversight. This is especially true in the field of Delegated Authority (DA)…
Managing General Agents (MGAs) provide insurers a broad opportunity for profitable growth. Operating in niche sectors with lean efficient models and providing customers with products that they often find hard to come by in the mainstream. All of this is wrapped up with the relevant expertise and providing insurers with access to markets that…
As 2022 was drawing to a close, insurers across the UK were starting to look at their results, keeping their fingers, toes and everything else that they could, crossed in the hope that December would fizzle out in a whimper.
But alas it was not to be as temperatures plummeted and the industry suffered…
What use statisticians and actuaries when the world is being transformed by modern data-analysis techniques? What hope for clunking humans in the era of Big Data (Facebook sells data which influences US presidential elections), of Artificial Intelligence (China can identify disliked racial groups through automatic facial recognition technology)…
Catastrophes – natural events where one “system” (wind, earthquake etc) causes multiple insurance claims in one go – are difficult. Much else in insurance pricing is subject to conceptually simple averaging-over-time, but cats are different. You can’t take a 5 or 10 year history of natural-peril losses and assume you have an accurate view of exposure to catastrophes…
In part 1 of this triptych on insurance pricing we looked at Technical Price (TP): what must be covered over the long run to make reasonable profits. I finished that note by considering TP in the context of the market. Here, I take this theme further and explore more how to think strategically about pricing.
This note covers the fundamentals: the three building blocks of a technical price and a note on the role technical price plays. It is followed with a deeper look at pricing strategies. Finally we consider a knotty topic, cat(astrophe) pricing, in particular the use of cat models to inform the price.
On 26th September 1983, at the height of the Cold War, Soviet missile monitoring picked up apparent signs of five nuclear missiles launched from the US. Retaliation was the default planned reaction.
Charles Dickens opened A Tale of Two Cities with these lines, contrasting calm enlightenment London with turbulent revolutionary Paris: “It was the best of times, it was the worst of times…
Calibrant was established to bridge a gap. The gap is that between MGAs and their carriers. And it finds many forms.