But for many, this promise is being hindered by a foundational legal concept: The Principle of Indemnity.

By insisting that property insurance must always be a contract of indemnity (meaning you cannot recover more than your actual, audited loss) regulators have forced the industry into a structural kludge known as the “Dual Trigger.” It’s a legal “fix” that satisfies the status quo but creates a cascade of inefficiencies for insurers and consumers alike.

The Mechanism of the “Dual Trigger”

In a rational parametric model, the data event is the payout. In the regulated world, however, two hurdles must be cleared:

1. The Data Trigger: The physical event occurs (e.g., wind speed, rainfall).
2. The Indemnity Proof: The policyholder must provide evidence that their actual loss equals or exceeds the payout.

This second trigger creates what we might call the Indemnity Trap. It caps the payout at the lower of the two values, fundamentally changing the nature of the risk.

Where the Principle of Indemnity comes from – and why it is a good idea in traditional insurance

Traditional insurance needs indemnity. It ensures the contract restores you rather than enriching you. In the non-life market, we insure the uncertainty of a loss. We don’t just insure the occurrence of an event.

If you could collect a payout that far exceeded your actual loss, you’ve moved from a safety net to a lottery ticket. This “Lotto Effect” turns insurance into a legally sanctioned wager. That windfall potential creates a toxic moral hazard. It invites fraud like arson or staged theft. It also rewards negligence. Why protect an asset when you are worth more if it burns?

By capping payouts at the Ultimate Net Loss, we align the policyholder’s interests with the asset’s survival. Insurance remains a stabilizing force. It protects wealth. It doesn’t generate profit from destruction.

The Problem: Asymmetric Basis Risk

This structure creates a profound misalignment. When we layer an indemnity cap onto a parametric trigger, we create a one-way street of risk:

• When the data misses: If the storm causes massive damage but the sensor doesn’t hit the trigger, the policyholder gets nothing. This is the “Negative Basis Risk” everyone acknowledges.
• When the data hits: If the sensor hits the trigger but the physical damage is light (perhaps because the owner invested in resilience), the indemnity rule steps in and caps the payout.

The result is a structure where the payout can be lower than the data suggests, but never higher. This isn’t a malicious choice by insurers; it is a structural constraint that leaves the risk transfer incomplete. It also reintroduces the very thing parametrics were meant to kill: payout delays. The moment you require a loss audit, the “instant cash” benefit of the parametric model is lost to the administrative friction of the indemnity process.

The Pricing and Underwriting Friction

This isn’t just a headache for policyholders; it complicates pricing.

To price a “clean” parametric policy, an actuary only needs weather data. But to price a policy with an indemnity cap, they must also predict the probability of the cap being hit. This requires traditional, granular underwriting of the asset. We’ve replaced a low-cost, scalable model with a high-cost, bespoke one, simply to satisfy a legal definition.

Assessing the Regulatory Responses

Why do regulators cling to the indemnity requirement? While the intentions are often centered on market stability, the logic behind these defenses deserves a closer look.

Argument 1: The Mitigation Incentive The traditional logic is that indemnity prevents moral hazard. The fear is that if people “profit” from a disaster, they will want the disaster to happen. However, this overlooks a critical reality of resilience. Traditional indemnity insurance actually discourages mitigation. If you spend your own capital to save your factory with sandbags, your indemnity payout simply drops to match your lower loss. In a parametric model without an indemnity cap, you are rewarded for that foresight. You keep the surplus as a “resilience dividend.” The current rules are, in effect, a structural barrier to climate adaptation.

Argument 2: Speculation vs. Insurable Interest There is a concern that without a proof of loss, insurance becomes a “Lotto” or a wager on the weather. But the gatekeeper against speculation should be Insurable Interest, not Indemnity. If a buyer demonstrates a legitimate economic exposure to the event at the point of sale, the speculative element is already addressed. We do not need a cumbersome audit at the back-end to solve a licensing and gatekeeping question at the front-end.

Argument 3: The Life Insurance Precedent It is often argued that property must be treated differently from life insurance because assets have a market value that must not be exceeded. Yet, the Life, Disability, and Critical Illness sectors function perfectly well as “valued contracts.” These are multi-trillion dollar industries that rely on Insurable Interest and a Reasonable Sum Assured. There is no fundamental logical reason why a crop, a solar farm, or a retail business could not be treated with the same “valued contract” logic we already apply to human life.

The Path Forward: The “Ought”

We shouldn’t be trying to “fix” parametric insurance by adding indemnity caps. We should be updating the regulatory framework to recognize Index-Based Insurance as a distinct legal category.

A modern, rational framework would require three things:

1. Provable Insurable Interest (Ensuring the buyer has skin in the game).
2. Reasonable Sum Assured (A cap based on total economic exposure, not just physical damage).
3. Objective, Independent Data Triggers that are demonstrably correlated with the risk exposure

The current “Dual Trigger” system isn’t a design choice; it’s a symptom of a regulatory system that hasn’t changed fast enough. I’d argue the regulations are focused too much on the potential cost and risk of change, while glossing over the downsides of not changing.

Is it time to stop forcing 21st-century risk tools into a 19th-century legal box?

Understanding the Basics: Banks vs Insurers

The classic banking model of liquidity risk is straightforward: banks transform short-term deposits into long-term loans. This maturity transformation creates inherent liquidity risk – even a perfectly solvent bank can face a crisis if too many depositors demand their money simultaneously. This fundamental risk drives the existence of central banks as lenders of last resort.

Insurance traditionally operated differently. With predictable claims patterns, regular premium income and unoptimised balance sheets, insurers weren’t thought to face significant liquidity risks. However, modern insurance practices and product designs have created more complex liquidity dynamics that challenge traditional frameworks. These liquidity-risk-increasing practices include some risk management choices (hedging and use of derivatives) and balance sheet sweating.

Sources of Liquidity Risk for Insurers

Insurance companies face several distinct sources of liquidity risk, some traditional and others emerging from modern practices:

Derivatives and Modern Asset Management

Modern investment strategies create significant liquidity demands:

• Use of illiquid assets through debt origination, greater use of corporate paper in general to provide higher yields for annuities and guaranteed/fixed bond products, private equity and other alternatives seeking additional yield
• Variation margin calls on derivatives require immediate cash as markets move
• Derivative roll risk creates periodic liquidity needs
• Rolling medium term corporate paper maturities into new instruments has elements of liquidity risk as part of the broader roll-risk universe
• Repo arrangements require careful liquidity management
• Hedging programs, while reducing other risks, increase liquidity demands

Policy Surrenders and Lapses

The liquidity impact of surrenders and lapses varies significantly by product type:

• Savings policies backed by liquid assets present limited liquidity risk
• Corporate policies often include liquidation notice periods
• Market value adjustments can share losses with policyholders
• Risk policies with negative liabilities create complex dynamics – while lapse might improve solvency ratios, the loss of positive cash flows can create future liquidity strains
• Loss of shareholder value is still likely the major risk for lapses and surrenders – and as a result it usually gets plenty of attention without the liquidity risk lens.

Internal Hedging and Optimisation

Insurance liquidity isn’t just about having assets to meet claims. Insurers often use positive cash flows from some policies (particularly risk policies with negative liabilities) to fund claims on other, especially older or maturing policies. This practice, while potentially efficient in normal times, creates hidden liquidity risks.

If these positive cash flows diminish (through lapses or reduced new business), the liquidity characteristics of the underlying assets become crucial. An insurer might appear to have strong liquidity based on expected premium inflows, but this can quickly change if those inflows reduce or stop.

Further, using negative liabilities (from profitable, early duration risk policies) to match positive ones (e.g., guaranteed savings products) creates hidden liquidity risk. This practice is another example of the “improvement” of an old, “lazy” matching approach that missed this opportunity for internal hedging, but perhaps reduces implicit buffers we may have come to rely on.

Claims Concentration

Sudden spikes in claims can create liquidity pressure:

• Natural catastrophes affecting property insurance
• Pandemic-related death claims
• Industrial accident clusters
• Legal or regulatory changes triggering multiple claims

Throughout these claim concentration risks, the performance of reinsurance and cash timing is also critical.

Premium Collection Disruption

Disruption to premium income can occur through:

• Economic downturns affecting customer ability to pay
• Operational disruptions to collection processes (South Africa experienced this a few years ago with the failure of a notable, concentrated exposure to a single premium collector)

Investment Portfolio Liquidity

Asset liquidity can become constrained through:

• Property/Real Estate holdings requiring time to sell
• Private equity/debt with limited secondary markets
• Complex structured products becoming illiquid in stress scenarios
• Market-wide liquidity stress affecting even traditionally liquid assets
• Money market fund holdings proving less liquid than assumed when stressed

Regulatory plans for improved liquidity risk management and reporting for insurers

Regulators are understandably keen to get a better handle on liquidity risk within the insurance sector – and are keen for insurers to take liquidity risk more seriously. Existing measures are widely considered imperfect (at best).

While we don’t want perfect to be the enemy of the good, there seems to be an opportunity to aim for better than current proposals.

The High-Quality Liquid Assets (HQLA) Paradox

A crucial distinction between banks and insurers lies in their access to central bank facilities. Banks can convert HQLA to cash via central bank discount windows, making these assets effectively cash equivalents. Insurers, lacking this access, face a different reality: even “highly liquid” assets can become illiquid during market stress. Insurers and other non-bank financial institutions may want access to the discount window, but my understanding is that this idea is a non-starter.

This creates an interesting regulatory paradox. Bank-style liquidity reporting, with its focus on monthly reporting, micro bucketing of asset maturities, but with implicit and assumptions about central bank access, may be suboptimal for insurers. Yet some regulatory frameworks still look to apply bank-centric thinking to insurer liquidity management.

Systemic Risk and Money Market Funds

A particular concern arises with money market funds, often assumed to be perfectly liquid. While an individual investor can usually liquidate their money market holdings easily, this isn’t true for the market as a whole. If the underlying instruments become illiquid, large-scale redemptions become impossible.

This creates a systemic risk: the appearance of liquidity in normal times masks the potential for market-wide liquidity crises. When multiple institutions rely on the same sources of apparent liquidity, the system becomes more fragile.

Testing Liquidity – Easier Said Than Done

Testing the ability to liquidate assets remains challenging. Current approaches to estimating liquidation costs are still maturing in many markets. Desktop exercises and historical analysis of liquidity crunches provide insights but have limitations.

Testing available liquidity by transacting in large volumes under normal conditions is expensive and, more importantly, tells us little about the ability to transact in disrupted markets. Tests of notional volumes may generate a false sense of security rather than inform real liquidation measures.

The true test of liquidity often only comes during stress events – precisely when you most need it to work.

When “Liquidity” Masks Solvency Issues

Some apparent liquidity crises are actually solvency issues in disguise. A prime example is minimum surrender guarantees in a rising rate environment. When interest rates rise significantly, policies with guaranteed surrender values can become deeply unprofitable. Each surrender crystallizes a real economic loss – no amount of liquidity support solves this underlying problem.

Policyholders can withdrawn their funds, benefit from the rising interest rate environment and re-invest in a new policy or other structure taking advantage of higher interest rates. It should be no surprise that this is the result of the dangerous combination of higher interest rates and guaranteed surrender values. (There are ways, complex, expensive ways, to manage this risk, but that first requires an appreciation of the risk. This requires at least adequate liability measurement, robust scenario testing that doesn’t assume prior low volatility periods will continue, and consideration of dynamic policyholder behaviour.)

Is this a liquidity risk? Firstly it is a solvency risk. Th value of “matching” assets has declined while the value of liabilities has not. A liquidity risk is only a liquidity risk if the provision of liquidity solves the problem.

When measuring liabilities and therefore solvency, it seems dangerous to rely on assumed policyholder irrationality (expecting them not to surrender when it’s clearly in their financial interest to do so) to support solvency calculations. Good risk management and appropriate liability measurement must recognize that policyholders will likely act in their financial interests, especially when the benefits of doing so become obvious.

Now there may also be a liquidity risk. If surrenders require liquidation of illiquid assets that may further depress asset prices, increasing yields and/or spreads. Resultant concerns around insurer solvency can also lead to a run on the insurer. It’s a mistake to think of all of this as a liquidity risk.

Implications for Risk Management

Liquidity risk is real, and may still be underestimated by many insurers. Insurers should be carefully evaluating their risk management systems for adequate coverage of liquidity risk.

These complexities demand sophisticated risk management approaches:

• Regular stress testing must consider both solvency and liquidity impacts
• These stress tests must be severe enough and must consider interactions
• Liability measurement needs to incorporate realistic policyholder behavior assumptions
• Investment strategies must balance efficiency with liquidity needs
• Liquidity buffers should consider both immediate and slow-burn scenarios
• Risk frameworks must recognize the limitations of market liquidity assumptions
• Consider when your sources of liquidity (money market fund contractual promises) may necessarily fail in systemic liquidity challenges

With a hefty additional dose of approximations, I get about the same figure all the way back to 1998.

This amounts to an insurer failing every other year.

The primary causes? In every case it’s more than one thing. Here are some of the recent common causes – I’ll expand on each of these in a series of posts.

1 Underwriting risk and pricing

Mispricing, particularly when moving into new markets or new lines of business is a common starting point.

Funeral insurers feeling competitive pressures are looking for new markets – typically semi-underwritten life products, misguided savings products, niche legal expense cover products, or further afield into non-life proper. Here be dragons.

For all the benefit of diversification from a statistical perspective, the research says that focussed insurers fail less often.

Climate change is going to break underwriting and pricing models, meaning that even previously well understood risks increase the chance of failure.

Non-life insurers need to get claims inflation under control – or at least continue the unpopular premium and excess increases to restore sustainability to premium rates.

2 Cost of customer acquisition outstripping funding and VNB

Rapid growth may be many insurers’ dreams.

However, too rapid growth can strain capital adequacy. Rapid growth can also be a telltale sign of under-pricing, leading to large volumes of unprofitable business. Selling many policies that don’t cover their acquisition expenses is a short cut to real trouble.

A worrying sign here is the reduction in VNB margins across broad sectors of the underwritten life insurance space. This ramps up pressures to dilute new business metrics, which is a terrible idea.

3 Misuse, and misrepresentation of (financial) reinsurance

Reinsurance is a fundamentally important tool to manage risk, manage capital requirements, gain expertise in a new market, and to provide liquidity.

Reinsurance, especially financial reinsurance when misused, can obscure the deteriorating solvency position of an insurer and lead to a false sense of security for risk managers, NEDs, and regulators.

The principles on how to treat financial reinsurance and contingent commissions are about right – but the detailed rules and the rigour and honesty with which those principles are implemented sometimes are not.

The overall lesson is – the improvement in your solvency should reflect the actual risk transferred and economics of the transaction.

The most egregious error is claiming that a FinRe deal has resulted in an increase in assets without an increase in liabilities. Tricks of claiming that repayment of the commission (a loan) is contingent on future profits and therefore isn’t a liability are invalid. Games with contract boundaries include recognising the upfront commission (which is to be repaid over many years of renewing contracts), but not recognising years of future reinsurance premiums because the in-force policies have annual contract boundaries.

On contingent commissions, the key question to ask is “has my SCR gone down by more than the risk transferred?”. If one reinsures 70% of the portfolio using QS, but 90% of that risk comes back through contingent commission, then applying the FSIs blindly can result in a 10x overstatement of the benefit of reinsurance. You have shared 7% of the risk, not 70%.

My rule of thumb is not to take advice on the regulatory, solvency, or accounting treatment of the reinsurance from the one selling you the reinsurance.

4 Complex, incestuous asset transactions, and poorly controlled ALM

Aggressive asset valuations, typically of unlisted, illiquid investment that have some related party in the mix, are one of the clearest red flags for an insurer about to fail.  There is always the next Warren Buffet wanting to “invest the float† and make money in some undeveloped property, associated business, or beautiful basket of tulips.

Careful ALM is critical for long-tailed policies. There it needs to be managed carefully and regularly. Monitoring isn’t enough – there needs to be a mechanism to change the portfolio when mismatch parameters breach thresholds.

For other portfolios, sometimes a simpler portfolio that introduces less complexity, fewer tax risks, less operational and liquidity risks, is better than a supposedly more ALM-tuned portfolio that actually increases risks of catastrophic failure.

Asset concentration has been a primary cause of at least one major South African insurance failure before too. Although, as always, this wasn’t the single cause.

5 Taking large (binary) risks when already in trouble

As solvency positions decline, some CEOs, seeing the writing on the wall, choose to take significant risks that will either solve their solvency problem, or increase the impact of insolvency to policyholders.

Something as simple as continuing to write business, especially long-term business, when the solvency capital isn’t available to support this business places existing and new policyholders under additional risk.

Pinning hopes (and management bandwidth) on big-bang investment deals without addressing underlying operational concerns usually don’t pay off.

6 Failed corporate governance

Corporate governance failures are usually the second or third thing to go wrong. Poor internal controls, ineffective or insufficiently independent risk and compliance teams, and outright financial statement fraud mean that serious problems are overlooked, sometimes for years.

Fraud is more often a response to problems (especially where management believes they are in the right and it’s just a matter of time before markets/the cycle/business turns). In select cases, insurers are used as vehicles to instigate fraud as first step

Some boards and shareholders deprioritise good governance. When times are good it’s easy to emphasise good governance. What about when governance gets in the way of decisions executives want to make? Or when it raises awkward questions about pet projects? Or where the business is struggling but management is confident they can trade out of the difficulty as long as they are given the space and time?

It’s easy to do the right thing when it doesn’t come with costs.

7 Slow regulatory intervention

Too often, regulatory intervention is too slow and not targeted at the underlying causes. It’s hard to blame the regulator entirely, given the massive opposition to statutory managers and curatorships.

There are many amazing, skilled, and experienced individuals at our regulator. Are there enough? Is the quality and approach consistent? Are they hamstrung by insurers under resourcing their own control functions and lines of defence?

Can anything be done to decrease failure rates?

Having a strong, experienced, and independent actuary who pays close attention to the regulations and guidance is crucial. Your head of actuarial function should provide good advice on business issues. They should also occasionally constrain your options and make you rethink your positions.

A solid, experienced, and independent Head of Actuarial Function goes a long way.

Appropriate risk management and governance practices are defined in multiple different places, and they can all work well enough if followed diligently. Making sure the teams are experienced and skilled and empowered to tell truth to power is rather more difficult.

But trying to imagine scenarios, particularly very severe scenarios, isn’t straightforward. We don’t have many examples of very extreme events.

Some insurers will dream up scenarios from scratch. It’s also common to refer to prior events and run the current business through those dark days. The Global Financial Crisis is a favourite – how would our business manage under credit spread spikes, drying up of liquidity, equity fall markets, higher lapses, lower sales, higher retrenchment claims, higher individual and corporate defaults, switches of funds out of equities, early withdrawals and surrenders and increased call centre volumes?

Downwards counterfactual analysis is the:

• analysis
• of events different (counter to) actual facts
• that are worse (downwards) than reality

For stress testing, we could take a past scenario and make it worse. What might have happened in 2007/2008 if African Bank had failed then, rather than a few years later? What would have been the result of an SA government sovereign downgrade at the same time, or if a major insurer got their hedging wrong enough to be really in trouble? What if the Cape Town water crisis hit at the same time and resulted in a cholera outbreak?

Some of those options are natural progressions of the factual scenario, whereas others (water + cholera) are mere add-ons.  The strongest sort of counterfactual analysis is where the original scenario was better than it might have been due to good luck. Tweaking that to an equally likely or even more likely but worse scenario powerfully shows how close we came to real disaster. A change in the prevailing wind during the Fukushima nuclear disaster is an obvious one.

Lloyds and RMS have put out a fascinating paper on downwards counterfactual analysis called “Reimagining history”.

It’s interesting stuff and well worth a full read.

Now, South Africa has been placed on high alert for a potential plague infection.

Mortality rates are estimated anywhere between 30% and 100% without treatment. Many estimates are towards the top end of this range, 80% to 95%. Treatments are available (mostly antibiotics) and are generally effective. Mortality rate where adequate treatment is administered within 24 hours can be 11%.  (Either “just 11%” or “11%!” depending on whether you’re counting up from 0% or down from 95%.)

Plague in Madagascar

124 people have already been killed by the plague in Madagascar. But this is just a particularly bad year.

Every year for the last nearly 40 years, Madagascar has a seasonable outbreak of plague. This is good news in that treatment of the disease and management of its spread is well understood.

Spread of Plague

Bubonic plague, which is more common, is spread mostly by flea bites and is not particularly contagious between humans. However, it can be transmitted between humans and when it infects the lungs to become pneumonic plague, it is far more infectious.

The good news is that it probably isn’t really a major threat even with impaired sanitation in Cape Town likely from water shortages.

What about Cholera?

Despite my concerns about cholera arising out of our water crisis, it is worth noting that the 2008-2009 outbreak in Zimbabwe claimed under 5,000 lives. As tragic as each of those deaths is, it is not the hundreds thousands or millions experienced in centuries past.

From a societal perspective, these levels of pandemics are very serious. The impact on the insured population, especially when weighted by sums assured and death claims, is still likely very low. (Although catastrophe reinsurance will not cover these days. QS and Surplus are the key lines of defence here from a reinsurance perspective.)

Aside

(If you like the idea of working tirelessly to rid the word of pandemics, check out Pandemic Board Game. If you lean more evil mastermind direction, Plague is a game where the goal is to infect the entire world.)

In this post I will cover these and other cats, the place of reinsurance including “feelings” and why this is hyper relevant for captives.

To demonstrate how even large general insurance catastrophes typically have no systemic implications for South Africa, I referenced the “2017 fire and storm claims”.

2017 Western and Southern Cape storm and fire claims

The media reported the following on these claims:

“Santam noted that the total insured damage has been estimated at around R3 billion, with economic losses (taking uninsured property into account) at significantly higher levels.

This was by far the worst catastrophe event in South African insurance history, with Santam client claims totalling around R800 million, of which R72 million related to the Cape Town property damage.”

So that all seems very intense. But then the story continues.

The Santam group weathered ‘the worst catastrophe event in South African insurance history’, to report growth of 14% for the six-month period ended June 2017.  Underwriting margin declined to 4.2% still within 4% to 8% range.

So we are left with the “worst catastrophe event” and underwriting results for Santam are still within range.

Which 2017 storms was that?

My slides for the convention had to be locked down some weeks back. So I was unable to include the other 2017 storms, in Gauteng and KZN.

According to this moneyweb article on the storms and insurance damages, might be between R1bn and R1.5bn for the industry. Those numbers are for the industry as a whole, so although they are higher than the R800m of claims anticipated by Santam for the earlier storms and fires, they are still major catastrophes all the same.

It’s hard to say precisely whether these claims, frequency and severity, have been influenced by climate change. The consensus is for more extreme weather all around.  While in any individual year this might mean more claims for insurers, I don’t believe it is bad for insurers overall. Insurers can adjust premiums to take the risks into account, and purchase reinsurance to protect against the damages.

On the contrary, as individuals and businesses and governments are exposed to greater risks, arguably the need for insurance increases.

(That’s a very different point from the damage of climate change and weather patterns being terrible for uninsured people and society as a whole.)

The place of reinsurance

Clearly, reinsurers picked up a significant tab for those claims. There was the usual debate around one vs two separate events, given the storm and winds that led and fanned the fires around Knysna. Appropriate use of reinsurance is so much more critical in the P&C / general insurance / short term insurance market than it is in most life insurance contexts.

The soft side of reinsurance optimisation

Adequate reinsurance, rational reinsurance modelling and optimisation  – and careful, considered and conscious risk appetite setting is key here. What I mean by that is, trying to understand how risk managers, Exco, Risk Committees and the Board will feel and react to actual claims not protected by reinsurance, either for moderately large claims within retentions, or massive claims (or multiple horizontal aggregations) above limits. Much of the modelling can be hyper-rational and informative, but the step of understanding the implications is often missed before the first event.

Even more critical for Captives

This is often an uncomfortable step for new Board members, or for representatives on Captive Insurer Boards less familiar with the significant risks and heavy reliance on reinsurance common in those structures.

Failure of reinsurers and systemic risk?

A question asked after my convention presentation was, what about the failure of reinsurers to pay out these cat claims?

There are two possibilities here:

1. The claims are so large that the highly rated, international reinsurers cannot pay. We are fortunate in South Africa given the size of our market and our relatively low risk from truly serious catastrophes that I simply don’t see this happening. The scenario of concern might be a major catastrophe at a future date after much-weakened international reinsurers were already teetering on the brink after a serious of climate-change or financial crisis impacts on solvency. Presumably as reinsurers’ credit worthiness was declining, appropriate risk management at local direct writers would be considering alternative solutions, including capital market options for risk transfer and limited risk accepted.
2. Poorly worded or misunderstood reinsurance contracts mean the reinsurer is not obliged to pay the claims, resulting in a gap between risk accepted by the direct writer and risk protection provided by the reinsurer. This is a far more significant risk. It is an operational risk from a risk taxonomy perspective, but with obvious underwriting interactions. This is where legal and compliance are needed to support the actuarial and risk functions.

The result?

All in all, cat risks and claims seem to be on the rise. Insurers may ultimately benefit from this, but only if sound risk management and adequate reinsurance cover is in place.

• Make sure your reinsurance optimisation considers hard and soft factors;
• Monitor the capital strength of reinsurers…
• …and consider capital market alternatives; and
• Confirm that your reinsurance contracts themselves provide the cover you think they do

• The risk of contagion, where failure of an entity leads to distress or failures of others [micro prudential]
• The risk of an event that can trigger serious consequences for the real economy. [macro prudential]

If you are a narrowly focussed prudential regulator, you might be concerned with the micro-prudential view. After the Global Financial Crisis acted as a reminder of the Great Depression, the impact on the real economy of failures within the financial sector now cannot be ignored.

Individual entities can fail and, possibly, be well managed through their failure to limit the financial impact on customers. The failure of several entities could similarly be limited in the breadth of its impact. Mass job losses, decline in GDP, failure of businesses outside the financial sector and other leakage into the real economy has costs measured as percentage of GDP foregone, likely over several years. Inevitably this a much larger hit than the failure of even several entities.

I don’t view this as a choice, either the micro view or the macro view. The failure of several entities is more likely to drive real economy effects. As I’ll discuss when in a later post when I talk about “Tsunamis”, no individual entity has to fail for their to real economy implications. Similarly, the failure of several financial services entities that doesn’t have significant real economy impacts is clearly undesirable.

The most useful view is of both of these as systemic risks and outcomes to be avoided.