Category Archives: Insurance Market

Softening but not soft

The speed dating jamboree that is the annual Monte Carlo Reinsurance Rendezvous kicks off on the 6th September where buyers and sellers (i.e. underwriters and brokers, with those clients that can afford the hotel rates) of speciality insurance and reinsurance kick off their discussions ahead of drawn-out negotiations for the January 2026 renewal season. Each side will earnestly set out their stall, with the US hurricane season as the backdrop (with the extent of any insured catastrophe losses playing a part in how actual negotiations progress towards the year-end), on the degree of rate, term, and condition changes for each client and/or client group.

The fact that rates have peaked is now beyond doubt with even the losses from the California wildfires failing to shift the downward trend in rates. In January, David Flandro of reinsurance broker Howden Re stated “if it wasn’t obvious already, we are now firmly in the hard market softening phase of the rating cycle” with the exhibit below in a report entitled “Passing the Pricing Peak” illustrating the point.

Metrics from Lloyd’s H1 presentation by new CEO Patrick Tiernan focus on the adequacy of rates in the context of the recent decline in rates, as per the exhibit below.

(Re)insurers and other commentators such as rating agencies are voraciously stressing the need for market discipline. A common defence from (re)insurers, as articulated by Flandro, is that “if you look at all lines, or most lines, we are still harder in terms of pricing than we were five years ago”. AM Best stated that “the lessons of past cycles suggest caution, but reinsurer sentiment has ensured tighter exposure management and market disciple in the current cycle” and asked, “the question now facing the industry is whether the improvements in terms and conditions represent a durable shift”. Fitch adjusted its outlook for the reinsurance sector to “deteriorating” stating that “softer pricing conditions and rising claims costs will pressure underwriting margins, though profitability remains strong by historical standards.” Munich Re CEO dismissed any talk of any meaningful softening in rates, stating “there is no soft market”. On the buyer side, Gallagher Re CEO highlighted that for property CAT business supply is now “materially outpacing demand”. There will be many such perspectives aired in articles and interviews over the coming week laying out the battle lines in these pre-negotiations.

A pickup in M&A is another sign that firms understand growth will not come from rates. Within the past few weeks, Sompo announced a deal to purchase Aspen at 1.3 times the tangible book and Skyward announced a deal for the Apollo Managing Agent in Lloyd’s at approx 8.5 times EBITDA, both sensible prices.

On my part, I will offer some of my thoughts on the subject in the remainder of this post through the lens of results and data over past decades, whilst updating some of the previous thinking detailed on this site (which are several years old now).

The first issue is to highlight the level of profitability the specialty insurance and reinsurance sector has enjoyed over the past few years. The exhibit below illustrates the heightened levels of ROE achieved by reinsurers over the cost of capital in recent years, according to a recent Gallagher Re mid-year report. Many would argue that such returns of 12-10% above the cost of capital are justified to compensate for the heightened risk environment of today given the climate, geopolitical, and macroeconomic issues at play.

By way of further illustrating the level of recent profits in the sector, Lloyd’s of London has just announced their H1 2025 results and the pre-tax profits of Lloyd’s from 2023, 2024 and H1 2025 equal the aggregate profit and losses from the previous 15 years (from 2007 to 2022). Lloyd’s has not had the two back-to-back return on capital years of 20%+ it had in 2023 and 2024 since 2006 and 2007.

The introduction of IFRS 17 reporting for many specialty insurers and reinsurers has complicated comparative, historical and aggregate analysis in the sector so I will revert to Lloyd’s historical results as a benchmark for the sector’s history. Obviously, Lloyd’s results carry a significant degree of caution when used as a proxy for the whole sector and I would caveat their use by referring to a (now 10-year-old) post called “Lessons from Lloyds”. An updated breakdown of the Lloyd’s combined ratio from 1993 to H1 2025 is below.

 30 year history of Lloyd's combined ratio, history of Lloyds of London

To illustrate the specialty underwriting cycle, I have discounted these combined ratios to adjust for the time value of money applicable for each year (i.e. a discount factor equal to the average annual 1 year T-Bill rate for each year over a duration of 24 months). The next metric I used to represent changes in market rates is the Guy Carpenter ROL index for property CAT reinsurance. Although this is clearly not representative of all specialty lines (see pricing and rate exhibits above) it gives a directional sense of rates for the overall sector, and it is measured on a consistent basis over an applicable long-term period. Combining these metrics with the inflated historical CAT insured losses for the post preceding this one gives the following graphic.

Specialty insurance cycle

In a highly unscientific way, I judgementally selected an ROL index base of 250 for the graphic as representing a level of adequacy akin to an 85% discounted combined ratio (as per 1996, 2003, 2009, and 2013). A 250 base indicates that the current 2025 index level has a further 7% to fall before becoming “inadequate”. This selection does assume that the sector has historically been able to adjust T&Cs, specifically attachment levels, to stay ahead of trend changes in insured CAT losses (at approx. 6-9% per year recently) due to factors such as loss inflation and climate change (an obviously BIG assumption!).

So, what does the above graphic illustrate? Accepting the (vast) limitations of my simplistic analysis, it indicates that the market today is at a similar stage in the rate cycle as we were in 2007 and 2014 (I would discount the 1990’s as the wild west in terms of London market underwriting behaviour). However, as highlighted by Beazley CEO Adrian Cox “in contrast to the previous softer cycle, there is a fundamental difference in today’s environment; the claims environment is active in respect to both frequency and severity, and uncertainty is elevated”. In the 2007 and 2013 years and the years that followed each, insured CAT losses were relatively low which fed the subsequent declines in rates. The CAT losses of 2011 and 2017 represented the bottom of each of the soft cycles albeit that the peak of 2012 and 2013 were short lived and it took a full 5 years over 2018 to 2022 for rates to get to an “adequate” level again.

Most (re)insurers would agree wholeheartedly in public with Cox when he says, “rate discipline is essential”. However, I suspect brokers and clients in Monte Carlo will be pushing hard to reduce rates further given the level of recent profits from the sector. Discipline but not yet will be the mantra and the level of insured losses (CAT or otherwise) over the remainder of 2025 will, I suspect, dictate how much softening of the current hard market will actually result in the January 2026 renewals and through 2026.

As a postscript, I also updated the graphic on underwriting and credit cycles to see if there were any further insights to be had, as below.

Specialty underwriting and credit cycle

The first thing to note is that credit and insurance cycles can be driven from the same event – 9/11 and COVID are obvious applicable cases. The graphic shows that the credit cycles over the past 20 years do not obviously influence the underwriting cycles with insured CAT losses being a much more relevant factor in the underwriting cycle.

The lack of a rush of new capital into the sector following major loss years have been an important factor over the past 20 years in shaping the character of underwriting cycles although economic and interest rate cycles do influence the level of capital which comes into the ILS market. It is interesting to note that with interest rates on a downward track currently, the appetite for CAT related returns from investors is again playing a part in the current availability of reinsurance, particularly retro, capacity. Memories can be short and it looks like it may take more CAT losses to reinforce to current ILS investors the risk they are taking on and the curse of “uncorrelated” tail exposure.

Finally, the impact of the policies of the mad Orange King and his sycophants, whom a wise commentator recently generously called “economic morons”, may just result in a return of a big beautiful credit crash akin to those of the past in the coming months and years which, were it to occur, would undoubtedly negatively impact everybody including (re)insurers.

It will be intriguing to see exactly how the 2026 renewal negotiations play out over the coming weeks and months.

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Historical CAT Insured Losses – an update.

I was recently doing some research on the specialty insurance sector again, a topic I posted regularly on in the past. I googled historical insured catastrophe losses and a response from Google’s AI model Gemini included an old exhibit I had posted on this blog in 2013. I am in two minds about the result, chuffed that something I posted 12 years ago is still being used but perplexed why an exhibit that was so out of date would be relevant! A subject for another day…..

Anyway, the below exhibit updates the inflated insured catastrophe losses from 1990 to 2024 (with Swiss Re’s estimate for 2025). The trend is clearly upwards with the new 10-year average at $130 billion and the 5-year average at $140 billion. This is a significant change from the $60 billion 10 year average in the 2013 post!

As I have highlighted many times previously here, inflated losses (i.e. bringing historical costs into today’s value) are not a true indicator of current risks as the historical losses need to be exposure adjusted (i.e. historical events run through models with today’s exposure date).

An excellent recent example of this is from a recent paper by Karen Clark & Co called “The $100 Billion Hurricane” which runs each historical US hurricane through 2025 exposures, as below.

The paper concludes that “there is no significant upward trend in hurricane losses, and the US has been lucky over the past few decades”.

Two different angles of looking at historical data albeit that it’s undeniable that catastrophe losses, both by economic and insured value, in aggregate each year are only going in one direction.

Let’s hope the remainder of the 2025 US hurricane season doesn’t show us that the single $100 billion hurricane loss was overdue!

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Multiple Temptation

I thought it was time for a quick catch up on all things reinsurance and specialty insurance since my last post a year ago. At that time, it looked like the underlying rating environment was gaining momentum and a hoped-for return to underwriting profitability looked on the cards. Of course, since then, the big game changer has been COVID-19.

A quick catch-up on the 2019 results, as below, from the Aon Reinsurance Aggregate (ARA) results of selected firms illustrates the position as we entered this year. It is interesting to note that reserve releases have virtually dried up and the 2019 accident year excluding cats is around 96%.

The Willis Re subset of aggregate results is broadly similar to the ARA (although it contains a few more of the lesser players and some life reinsurers and excludes firms like Beazley and Hiscox) and it shows that on an underling basis (i.e. accident year with normalised cat load), the trend is still upwards and more rate improvement is needed to improve attritional loss ratios.

The breakdown of the pre-tax results of the ARA portfolio, as below, shows that investment returns and gains saved the day in 2019.

The ROE’s of the Willis portfolio when these gains were stripped out illustrates again how underwriting performance needs to improve.

Of course, COVID-19 has impacted the sector both in terms of actual realised losses (e.g. event cancellations) and with the cloud of uncertainty over reserves for multiple exposures yet to be fully realised. There remains much uncertainty in the sector about the exact size of the potential losses with industry estimates ranging widely. Swiss Re recently put the figure at between $50-80 billion. To date, firms have established reserves of just over $20 billion. One of the key uncertainties is the potential outcome of litigation around business interruption cover. The case brought in the UK by the FCA on behalf of policyholders hopes to expedite lengthy legal cases over the main policy wordings with an outcome expected in mid-September. Lloyds industry insurance loss estimate is within the Swiss Re range and their latest June estimate is shown below against other historical events.

I think Alex Maloney of Lancashire summarised the situation well when he said that “COVID-19 is an ongoing event and a loss which will take years to mature”, adding that for “the wider industry the first-party claims picture will not be clear until 2021”. Evan Greenberg of Chubb described the pandemic as a slow rolling global catastrophe impacting virtually all countries, unlike other natural catastrophes it has no geographic or time limits and the event continues as we speak” and predicted that “together the health and consequent economic crisis will likely produce the largest loss in insurance history, particularly considering its worldwide scope and how both sides of the balance sheet are ultimately impacted”.

The immediate impact of COVID-19 has been on rates with a significant acceleration of rate hardening across most lines of business, with some specialty lines such as certain D&O covers have seen massive increases of 50%+. Many firms are reporting H1 aggregate rate increases of between 10% to 15% across their diversified portfolios. Insurance rate increases over the coming months and reinsurance rates at the January renewals, assuming no material natural cats in H2 2020, will be the key test as to whether a true hard market has arrived. Some insurers are already talking about increasing their risk retentions and their PMLs for next year in response to reinsurance rate hardening.

Valuations in the sector have taken a hit as the graph below from Aon on stock performance shows.

Leaving the uncertainty around COVID-19 to one side, tangible book multiples amongst several of my favourite firms since this March 2018 post, most of whom have recently raised additional capital in anticipation of a broad hard market in specialty insurance and reinsurance market, look tempting, as below.

The question is, can you leave aside the impact of COVID-19? That question is worthy of some further research, particularly on the day that Hiscox increased their COVID-19 reserves from $150 million to $230 million and indicated a range of a £10 million to £250 million hit if the UK business interruption case went against them (the top of the range estimate would reduce NTAs by 9%).

Food for thought.

Creepy Things

It has been a while since I looked at the state of the reinsurance and specialty insurance markets. Recent market commentary and insurers’ narratives at recent results have suggested market rates are finally firming up, amidst talk of reserve releases drying up and loss creep on recent events.

Just yesterday, Bronek Masojada the CEO of Hiscox commented that “the market is in a better position than it has been for some time”. The Lancashire CEO Alex Maloney said he was “encouraged by the emerging evidence that the (re)insurance market is now experiencing the long-anticipated improvements in discipline and pricing”. The Chubb CEO Evan Greenberg said that “pricing continued to tighten in the quarter while spreading to more classes and segments of business, particularly in the U.S. and London wholesale market”.

A look at the historical breakdown of combined ratios in the Aon Benfield Aggregate portfolio from April (here) and Lloyds results below illustrate the downward trend in reserve releases in the market to the end of 2018. The exhibits also indicate the expense disadvantage that Lloyds continues to operate under (and the reason behind the recently announced modernisation drive).

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In the Willis Re mid-year report called “A Discerning Market” their CEO James Kent said “there are signs that the longstanding concern over the level of reserve redundancy in past year reserves is coming to fruition” and that in “some classes, there is a clear trend of worsening loss ratios in recent underwriting years due to a prolonged soft market and an increase in loss severity.

 In their H1 presentation, Hiscox had an exhibit that quantified some of the loss creep from recent losses, as below.

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The US Florida hurricane losses have been impacted by factors such as assignment of benefits (AOB) in litigated water claims and subsequently inflating repair costs. Typhoon Jebi losses have been impacted by overlapping losses and demand surge from Typhoon Trami, the Osaka earthquake and demand from Olympics construction. Arch CEO Marc Grandisson believes that the market missed the business interruption and contingent BI exposures in Jebi estimates.

The fact that catastrophic losses are unpredictable, even after the event, is no surprise to students of insurance history (this post on the history of Lloyds is a testament to unpredictability). Technology and advances in modelling techniques have unquestionably improved risk management in insurance in recent years. Notwithstanding these advances, uncertainty and the unknown should always be considered when model outputs such as probability of loss and expected loss are taken as a given in determining risk premium.

To get more insight into reserve trends, it’s worth taking a closer look at two firms that have historically shown healthy reserve releases – Partner Re and Beazley. From 2011 to 2016, Partner Re’s non-life business had an average reserve release of $675 million per year which fell to $450 million in 2017, and to $250 million in 2018. For H1 2019, that figure was $15 million of reserve strengthening. The exhibit below shows the trend with 2019 results estimated based upon being able to achieve reserve releases of $100 million for the year and assuming no major catastrophic claims in 2019. Despite the reduction in reserve releases, the firm has grown its non-life business by double digits in H1 2019 and claims it is “well-positioned to benefit from this improved margin environment”.

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Beazley is one of the best insurers operating from London with a long history of mixing innovation with a balanced portfolio. It has doubled its net tangible assets (NTA) per share over the past 10 years and trades today at a 2.7 multiple to NTA. Beazley is also predicting double digit growth due to an improving rating environment whilst predicting “the scale of the losses that we, in common with the broader market, have incurred over the past two years means that below average reserve releases will continue this year”.

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And that’s the rub. Although reserves are dwindling, rate improvements should help specialty (re)insurers to rebuild reserves and improve profitability back above its cost of capital, assuming normal catastrophe loss levels. However, market valuations, as reflected by the Aon Benfield price to book exhibit below, look like they have all that baked in already.

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And that’s a creepy thing.

More ILS illuminations

A continuation of the theme in this post.

The pictures and stories that have emerged from the impact of the tsunami from the Sulawesi earthquake in Indonesia are heart-breaking. With nearly 2,000 officially declared dead, it is estimated that another 5,000 are missing with hundreds of thousands more severely impacted. This event will be used as an vivid example of the impact of soil liquefaction whereby water pressure generated by the earthquake causes soil to behave like a liquid with massive destructive impacts. The effect on so many people of this natural disaster in this part of the world contrasts sharply with the impact on developed countries of natural disasters. It again highlights the wealth divide within our world and how technologies in the western world could benefit so many people around the world if only money and wealth were not such a determinant of who survives and who dies from nature’s wrath.

The death toll from Hurricane Florence on the US, in contrast, is around 40 people. The possibility of another US hurricane making landfall this week, currently called Tropical Storm Michael, is unfolding. The economic losses of Hurricane Florence are currently estimated between $25 billion and $30 billion, primarily from flood damage. Insured losses will be low in comparison, with some estimates around $3-5 billion (one estimate is as high as $10 billion). The insured losses are likely to be incurred by the National Flood Insurance Program (NFIP), private flood insurers (surplus line players including some Lloyds’ Syndicates), crop and auto insurers, with a modest level of losses ceded to the traditional reinsurance and insurance-linked securities (ILS) markets.

The reason for the low level of insured loss is the low take-up rate of flood policies (flood is excluded from standard homeowner policies), estimated around 15% of insurance policies in the impacted region, with a higher propensity on the commercial side. Florence again highlights the protection gap issue (i.e. percentage difference between insured and economic loss) whereby insurance is failing in its fundamental economic purpose of spreading the economic impact of unforeseen natural events. Indeed, the contrast with the Sulawesi earthquake shows insurance failings on a global inequality level. If insurance and the sector is not performing its economic purpose, then it simply is a rent taker and a drag on economic development.

After that last sentiment, it may therefore seem strange for me to spend the rest of this blog highlighting a potential underestimating of risk premia for improbable events when a string of events has been artfully dodged by the sector (hey, I am guilty of many inconsistencies)!

As outlined in this recent post, the insurance sector is grappling with the effect of new capital dampening pricing after the 2017 losses, directly flattening the insurance cycle. It can be argued that this new source of low-cost capital is having a positive impact on insurance availability and could be the answer to protection gap issues, such as those outlined above. And that may be true, although under-priced risk premia have a way of coming home to roost with serious longer-term effects.

The objective of most business models in the financial services sector is to maximise the risk adjusted returns from a selected portfolio, whether that be stocks or bonds for asset managers, credit risks for banks or insurance risks for insurers. Many of these firms have many thousands of potential risks to select from and so the skill or alpha that each claim derives from their ability to select risks and to build a robust portfolio. If for example, a manager wants to build a portfolio of 20 risks from a possible 100 risks, the combinations are 536 trillion (with 18 zeros as per the British definition)! And that doesn’t consider the sizing of each of the 20 positions in the portfolio. It’s no wonder that the financial sector is embracing artificial intelligence (AI) as a tool to assist firms in optimizing portfolios and potential risk weighted returns (here and here are interesting recent articles from the asset management and reinsurance sectors). I have little doubt that AI and machine learning will be a core technique in any portfolio optimisation process of the future.

I decided to look at the mechanics behind the ILS fund sector again (previous posts on the topic include this post and this old post). I constructed an “average” portfolio that broadly reflects current market conditions. It’s important to stress that there is a whole variety of portfolios that can be constructed from the relatively small number of available ILS assets out there. Some are pure natural catastrophe only, some are focused at the high excess level only, the vintage and risk profile of the assets of many will reflect the length of time they have been in business, many consist of an increasing number of private negotiated deals. As a result, the risk-return profiles of many ILS portfolios will dramatically differ from the “average”. This exercise is simply to highlight the impact of the change of several variables on an assumed, albeit imperfect, sample portfolio. The profile of my “average” sample portfolio is shown below, by exposure, expected loss and pricing.

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The weighted average expected loss of the portfolio is 2.5% versus the aggregate coupon of 5%. It’s important to highlight that the expected loss of a portfolio of low probability events can be misleading and is often misunderstood. Its not the loss expected but simply the average over all simulations. The likelihood of there being any losses is low, by definition, and in the clear majority of cases losses are small.

To illustrate the point, using my assumed loss exceedance curves for each exposure, with no correlation between the exposures except for the multi-peril coverage within each region, I looked at the distribution of losses over net premium, as below. Net premium is the aggregate coupon received less a management fee. The management fee is on assets under management and is assumed to be 1.5% for the sample portfolio, resulting in a net premium of 3.5% in the base scenario. I also looked at the impact of price increases and decreases averaging approximate +/-20% across the portfolio, resulting in net premium of 4.5% and 2.5% respectively. I guesstimate that the +20% scenario is roughly where an “average” ILS portfolio was 5 years ago.

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I have no doubt that the experts in the field would quibble with my model assumptions as they are crude. However, experience has thought me that over-modelling can lead to false sense of security and an over optimistic benefit for diversification (which is my concern about the ILS sector in general). My distributions are based upon 250,000 simulations. Others will point out that I haven’t considered the return on invested collateral assets. I would counter this with my belief that investors should only consider insurance risk premium when considering ILS investments as the return on collateral assets is a return they could make without taking any insurance risk.

My analysis shows that currently investors should only make a loss on this “average” portfolio once every 4 years (i.e. 25% of the time). Back 5 years ago, I estimate that probability at approximately 17% or roughly once every 6 years. If pricing deteriorates further, to the point where net premium is equal to the aggregate expected loss on the portfolio, that probability increases to 36% or roughly once every 3 years

The statistics on the tail show that in the base scenario of a net premium of 3.5% the 1 in 500-year aggregate loss on the portfolio is 430% of net premium compared to 340% for a net premium of 4.5% and 600% for a net premium of 2.5%. At an extreme level of a 1 in 10,000-year aggregate loss to the portfolio is 600% of net premium compared to 480% for a net premium of 4.5% and 800% for a net premium of 2.5%.

If I further assume a pure property catastrophe reinsurer (of which there are none left) had to hold capital sufficient to cover a 1 in 10,000-year loss to compete with a fully collaterised ILS player, then the 600% of net premium equates to collateral of 21%. Using reverse engineering, it could therefore be said that ILS capital providers must have diversification benefits (assuming they do collaterise at 100% rather than use leverage or hedge with other ILS providers or reinsurers) of approximately 80% on their capital to be able to compete with pure property catastrophe reinsurers. That is a significant level of diversification ILS capital providers are assuming for this “non-correlating asset class”. By the way, a more likely level of capital for a pure property catastrophe reinsurer would be 1 in 500 which means the ILS investor is likely assuming diversification benefits of more that 85%. Assuming a mega-catastrophic event or string of large events only requires marginal capital of 15% or less with other economic-driven assets may be seen to be optimistic in the future in my view (although I hope the scenario will never be illustrated in real life!).

Finally, given the pressure management fees are under in the ILS sector (as per this post), I thought it would be interesting to look at the base scenario of an aggregate coupon of 5% with different management fee levels, as below. As you would expect, the portfolio risk profile improves as the level of management fees decrease.

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Given the ongoing pressure on insurance risk premia, it is likely that pressure on fees and other expenses will intensify and the use of machines and IA in portfolio construction will increase. The commodification of insurance risks looks set to expand and increase, all driven by an over-optimistic view of diversification within the insurance class and between other asset classes. But then again, that may just lead to the more wide-spread availability of insurance in catastrophe exposed regions. Maybe one day, even in places like Sulawesi.