How efficient is capital allocation in insurance?

An encouraging picture of an ever-more varied and specialised market is undercut by market-wide inefficiencies that impede the flow of capital to risk, writes William Pitt.

Reinsurance buyers are among the biggest spenders in insurance, shelling out hundreds of billions of dollars a year. Are they getting value for money?

As the industry gathers in Monte Carlo, the price of reinsurance is the stuff of a thousand conversations. The value of reinsurance is less frequently debated. But it’s instructive to look beneath the ebb and flow of pricing. The value that cedants see in reinsurance is part of a bigger picture, spanning the efficiency of capital allocation across an increasingly complex and specialised global insurance market.

Ben Rose has studied the flow of data in reinsurance markets closely. His experience spurred him and his former Aon colleague Jerad Leigh to found Supercede in 2019, offering a digital platform for cedants and their brokers to assemble submission packs that are easier for reinsurers to evaluate. In the absence of the necessary data, coherently presented, Rose says reinsurers will commonly add an “uncertainty load” to premiums of between 5 and 10 percent. And in the current hard market, they may decline to write business with poor submission data altogether.

Rose holds out a vision in which the high frictional costs of ceding reinsurance are massively reduced through automation of currently manual processes and, as a consequence, insurance companies buy much more reinsurance. In this way large swaths of the current risk-bearing insurance market could move closer to today’s asset-light MGA model, leaving the reinsurers as the last guardians of large balance sheets.

The view from Omaha

Insurance is often decried as a slow-moving, inefficient industry. Efficiency, however, can be hard to pin down. One definition is that it is a measure of the ratio of inputs to outputs. Fewer inputs for a given level of output equals a more efficient system. But what inputs and what outputs? For insights into this question, which has huge ramifications for the future relevance and affordability of insurance, Monte Carlo may not be the best locale.

Omaha, Nebraska is almost 5,000 miles from Monte Carlo and much less sunny, but each year it hosts an equally well-heeled rendezvous, that of Berkshire Hathaway shareholders. A telling exchange at this year’s gathering in May highlighted one way in which the insurance market is growing more efficient and one way in which it is not.

A Berkshire shareholder complained that the group’s auto insurance subsidiary Geico had not prioritised data and analytics in the same way as Progressive – and had suffered as a result. Berkshire’s vice chairman Ajit Jain admitted that Geico had fallen behind Progressive in matching rate with risk. He expressed optimism that, with the help of new talent, the company would catch up by the end of 2025.

Then Jain’s boss stepped in. While agreeing that matching rate with risk was important in every line of insurance, Warren Buffett noted that Geico’s ultra-low expense ratio (9.7 percent in 2023) remained a “fundamental advantage”. Harking back to a principle articulated by Leo Goodwin, the founder of Geico, in 1936, Buffett said: “If you can offer someone a cheaper product than the other guy and everyone has to buy it … it’s a very attractive business to be in.” At 19.8 percent, Progressive’s expense ratio in 2023 was more than twice as high as Geico’s.

So which company is the most efficient? By one measure, Geico appears the winner. Its inputs cost 10 percentage points less than its strongest competitor, enabling it to undercut the market on price, often by a large margin. But through a different lens, Progressive looks more efficient because the prices it charges are better calibrated to the risks it assumes and, in aggregate, this yields superior loss ratios. And with the help of greater advertising expenditure, Progressive has been growing far faster than Geico.

The two approaches to efficiency are not, of course, mutually exclusive, but nor are they perfectly complementary. Expense ratios and loss ratios often move inversely to one another. Few have wholeheartedly adopted the low-cost Goodwin playbook as endorsed by Buffett. In recent years, capital has generally favoured Progressive’s approach, supporting businesses that employ rich datasets to target lower loss ratios while paying less attention to expense ratios. The average expense ratio for the US property casualty market stood at 25 percent last year and has scarcely budged in more than four decades. This may be why the insurance industry has failed to grow faster than the growth rate of global GDP. Its value proposition to the market as a whole is not getting any more attractive.

But on the plus side, the better risks are often now being rewarded with cheaper coverage and, in some cases, with affordable coverage where none would have been available before. Plenty of insurers have become far more efficient in Progressive’s sense, making huge strides in matching risk to rate.

Rewiring the market

Many of these insurers are in fact MGAs (backed increasingly by reinsurers through fronting companies), which are drawing upon new and more granular data sources to price risk. The tools available to them are growing more sophisticated – software provider ZestyAI now offers a range of climate risk models to assess property risk with what it calls “surgical precision”. And in dislocated markets such as the California market for properties exposed to wildfires, MGAs such as Delos, Kettle and Bamboo have helped to fill coverage gaps left by the withdrawal of large insurance companies. Parametric insurers such as Paris-based MGA Descartes are constantly fine-tuning their pricing algorithms to expand coverage further.

MGAs are thus rewiring large parts of the insurance market. As an additional link in the value chain, they do not slot easily in the Geico model of low-cost efficiency. More like Progressive, they aim to match rate to risk better than competitors, usually through their superior understanding of niche markets – all for a handsome fee.

The rapid influx of MGAs has contributed to a smorgasbord of capital structures in the insurance market, catering to the shifting appetites of capital providers. For the past decade, asset-light options have been rewarded with soaring exit multiples that are now far higher than those commanded by most balance sheet businesses. But MGAs with confidence in their pricing, and the track record to prove it, have also won investor support for greater assumption of risk, whether through reinsurance captives, ownership of insurance companies, or participation in the Lloyd’s market. Bowhead Specialty, which operates an MGA and balance sheet insurers on what it calls a “nimble and efficient platform”, went public in May and has since traded well above its IPO price. MGAs with at least some skin in the game (though not necessarily as much as Bowhead) are often attractive to reinsurers as they offer greater alignment of interests.

From asset-light to asset-heavy – capital structures have multiplied

This broadly encouraging picture of an increasingly specialised market is undercut by major market-wide inefficiencies in capital allocation that persist. These include:

1. The glacial speed with which capacity must often be assembled to launch new MGAs or even new programs. Even with highly regarded underwriters touting a strong track record, capacity for a new MGA can take more than a year to negotiate in the US. Outside the US, it can take much longer. The founders of Element, a fronting company established in Germany in 2018 to support MGAs, originally wanted to form a pet insurance MGA themselves but were told by local carriers that it would take three or four years to do so.

2. Continuing problems with the sharing of data for complex risks – the issue Supercede is seeking to address. At present, few insurers share the keen focus on data transparency along the chain championed in Europe and the US by Accelerant, which – in contrast to normal fronting company practice – offers a one-stop shop to MGAs, with automatic, built-in reinsurance capacity that does not have to be separately negotiated.

3. Conflicts between the priorities of different capital providers. These can obscure the true value of a business – and imperil its survival. In particular, venture capital and private equity investors may have rapid growth ambitions for MGAs that are deeply unsettling to capacity providers.

The severity of these problems varies. Overpriced reinsurance may dent the profitability of an insurance company, but it will rarely prove an existential risk. By contrast, misaligned capital providers can spell doom for an MGA, as Koffie Financial, a tech-enabled MGA targeting the small trucking company market, discovered earlier this year.

Out of time

Koffie sold its technology to Acrisure after it was caught in a rip tide of diverging investor and capacity provider expectations, according to founder Ian White. “On the venture side, you’re raising maybe two years of capital at a time and you’ve got to show that you’ve got 200 percent growth, but eventually your reinsurers are going to say that 100 percent growth per year will lead to disastrous underwriting results,” he explained.

White had confidence in telematics to supply data that would reduce claim severity, but the cash flow strain of paying for the technology before it could reap a return in profit commissions hastened its demise. (White calls this the “insurtech paradox”.) And Koffie’s capacity crunch was worsened by the fact that a collateralised reinsurer on its panel had Vesttoo capacity that needed to be replaced.

Time was ultimately the resource that Koffie ran out of. This gives rise to a further insurance paradox. The market can assemble large volumes of risk-bearing capacity very quickly and commit it on a handshake. But that is because insurance is a trust-based business that – notwithstanding the data-driven growth of alternative capital – still relies heavily on personal relationships, built over time.

Seen from one perspective – that of a start-up MGA eager to access a promising market, for instance – this may not seem at all efficient. But from other perspectives it is indispensable.