His point is this: once agents like Meta’s Muse can move money, they could sweep household cash out of US checking accounts paying around 0.1% into accounts paying 3.3% to 5.0%.¹ ² Banks could lose some of the cheap deposits they rely on to lend, and as this is adopted more widely, could significantly change the funding patterns for banks. His example looks to the US, but similar analysis applies in many markets, including South Africa.

The first implication is agents enforcing better information and competition – more efficiency the micro economists will be pleased to note. Superficially, that might put upwards pressure on deposit rates, providing better value for banking customers (at least the depositors) at the cost of the bank or their borrowing customers. Lending rates take time to adjust. In the interim, higher deposit rates being offered may erode bank deposit spread or net interest margin. Thinner margins raise risk for banks too. (Higher risk because lower baseline profits makes it slower to rebuild capital, more likely to make losses, but also because it may prompt greater risk-taking to grow the margin.) Whether you want banks to be more or less profitable is your call. I’m conflicted (in both senses of the word) so I’ll stay quiet on that point.

Next: speed. Lower friction and constant comparison mean small rate differences can drive large flows. Deposits become less sticky. Some of this money stays in the banking system, moving from one bank to another, whereas others may go to money market accounts or that other major 2020s development, the stablecoin, and thus into US treasuries or other pool of generally low risk non-banking assets. The damage is to each individual bank’s funding stability, but potentially also the total available deposit base for the banking system as a whole. (Stablecoins, at least in the US, can’t pay interest, but there are grey areas here related to funds and rewards.)

Slok’s run is yield-driven sorting. But I want to take it a step further – and into risk.

If deposits are now less sticky, banks are more exposed to liquidity shortfalls. Greater risk of failure is definitely relevant to the decision of where to place funds, and agents are likely better suited to evaluating and re-evaluating this quickly. So agents won’t just chase yield. They will become risk-aware too. (I said risk-aware, not self-aware.)

As an individual, it is sensible to want an agent that moves your money into the right risk-adjusted accounts or funds, and quickly away from a bank perceived to be risky. What is logical for you is logical for everyone else. Today, our different information sources, different viewpoints, distractions and delays mean these decisions are for the most part poorly coordinated. Tomorrow, many agents, acting on the same triggers and signals, could pull money out of a single bank at something closer to the speed of high-frequency trading than of a Sunday newspaper reporting last quarter’s filings. Humans have already been getting faster – and I’m not talking about Usain Bolt. On 9 March 2023 Silicon Valley Bank lost over $40 billion of deposits in a day, and expected over $100 billion more the next.³

So agents optimising for yield could raise the risk of a run on certain banks, then respond in a correlated (if not coordinated) manner to that higher risk, amplifying and accelerating the risk.

Deposit insurance limits this, but only partly. A risk-aware agent might keep balances within the insured limit (US$250,000 in the US, R100,000 per depositor per bank under South Africa’s Corporation for Deposit Insurance).⁴ But just because deposit protection exists doesn’t mean there isn’t a frictional cost to being a depositor that wants money here, today and right now rather than wait for regulatory processes. Besides, deposit insurance has its own economics. There isn’t some infinite well of funds to bail out depositors. The point of deposit insurance is to reduce the failures in the first place. But at the limits imposed, it only protects small savers. It concentrates run risk in uninsured balances. Just to explain the scale of the mismatch of funds and insured funds: at Silicon Valley Bank, about 94% of deposits were uninsured.³ To be fair, SVB was an outlier. The peer average was around 41% at the time.

Banks face pressure from two directions. They must pay competitive rates to hold on to deposits. But this newly-volatile deposit funding is riskier than in the past, so they may prefer longer-term funding and be prepared to pay more for it, and less for the now unattractive overnight deposit. Deposit rates might therefore settle lower or higher than today. That depends on risk appetite and on supply and demand, including the impact of non-bank alternatives, the so called shadow banking world, and the impact of stablecoins (and central bank digital currencies in some parts of the world). I don’t think anyone can really know the equilibrium of this complex system in advance. Or if there even is something that could be described as an equilibrium rather than a dynamic moving target.

There is some familiar prisoner’s dilemma stuff going on here. For an individual, using an agent looks clearly positive. Up until many people use the same or correlated agents. Then it may be bad for the system as a whole. One outcome is more concentration in “too big to fail” banks, which carries its own costs. Another is individual bank failures. Either way the transition may be disruptive. But back to the individual prisoner – if the system is newly unstable and risky, isn’t that just a further argument to use an agent?

The difference this time is that every prisoner has hired the same lawyer.

¹ Torsten Slok, “Is an Agentic Bank Run Coming?”, Apollo, The Daily Spark, 27 September 2026. https://www.apollo.com/wealth/insights-news/insights/daily-spark/is-an-agentic-bank-run-coming

² Plaid, “Plaid powers Meta’s new AI agent, Muse”, 8 September 2026. https://plaid.com/blog/meta-muse/

³ Board of Governors of the Federal Reserve System, “Review of the Federal Reserve’s Supervision and Regulation of Silicon Valley Bank”, April 2023. https://www.federalreserve.gov/publications/files/svb-review-20230428.pdf

⁴ South African Reserve Bank, “CODI Frequently Asked Questions”. https://resbank.co.za/en/home/what-we-do/Deposit-insurance/CODI-Frequently-Asked-Questions

Except taxes aren’t certain. Not the amounts, not the timing, not the rules. And once you start pulling on that thread, the list of things we treat as certain, in our models and in our heads, gets uncomfortably long.

This is that list. It is not comprehensive, and it is not a claim that any of these things will change soon. The point is narrower and more useful: each item is something that felt impossible to imagine changing, right up until it changed. That feeling of impossibility is what this post is about, because the feeling is not evidence.

1. Taxes

South Africa introduced capital gains tax in 2001. Secondary tax on companies became dividends tax in 2012, at 15%, which became 20% in 2017. The corporate rate moved from 28% to 27% in 2023. VAT went from 14% to 15% in 2018, and in 2025 an announced further increase was scrapped after the political fallout, which is its own lesson: even the changes aren’t certain.

For life insurers the changes cut deeper. The four-fund tax basis arrived in 1993, and the risk policy fund was added from 2016, moving individual risk business from the I minus E basis to a corporate-style tax on profits. Anyone who had projected the tax cash flows of a risk book beyond 2016 on the old basis was simply wrong, through no fault of their modelling.

Yet most projection models take this year’s Income Tax Act and apply it to 2060. The amount of tax, the timing of tax, and the rules of tax all change, and they change more often than almost any other assumption we hold fixed.

2. Currency pegs

I worked in Lebanon in 2007. Insurers there held US dollar assets against Lebanese pound benefits, and pound assets against dollar benefits, and told me not to worry. There was no currency risk. The pound was pegged.

They had history on their side. The pound had been fixed at 1,507.5 to the dollar since 1997, a decade of perfect stability by the time I arrived, and it went on to hold for another twelve years. Then, from 2019, it lost more than 98% of its value, and the collapse took the banking system and those mismatched balance sheets with it. Dollar deposits became “lollars”, visible on the bank statement, inaccessible in practice.

Lebanon is not an isolated case. Sterling left the gold standard in 1931. The United States closed the gold window in 1971, ending Bretton Woods. Sterling was forced out of the ERM in 1992. Argentina’s one-to-one convertibility, in place for a decade and written into law, collapsed in 2002. And the Swiss franc’s floor against the euro, not even a peg, just a floor, was abandoned in January 2015 and the franc moved close to 20% in minutes, against the largest currency bloc in the world, in the middle of a trading day.

Against that record sits the Namibian dollar, pegged to the rand since 1993, along with the loti and lilangeni. Those pegs are fine. So far. Every peg on the broken list above was also fine, so far, right up until it wasn’t.

Judging pegs by the ones still standing is exactly the error. These are right truncated observations on data periods of hundreds of years. A peg that hasn’t broken is a censored data point, not a proof of safety. “Still pegged” only means the failure hasn’t been observed yet.

3. The biggest companies

None of the ten largest US companies of 50 years ago is in the top ten today. In 1975 the list was Exxon, General Motors, Ford, Texaco, Mobil, Chevron, Gulf Oil, General Electric, IBM and ITT. Oil and cars and machines, each one an institution nobody expected to be displaced.

Some fell further than merely out of the top ten. Kodak, a giant of that era, went bankrupt in 2012. Sears followed in 2018. General Electric, an original member of the Dow in 1896 and a continuous member from 1907, was removed from the index in 2018 after more than a century.

The investment crowd knows this statistic. It appears in every presentation on diversification and every argument for passive investing. But knowing it and believing it about today’s top ten are different exercises. Each of the 1975 giants looked as permanent then as the current ten look now, and the current ten look very permanent indeed.

4. Government debt

An A rating is meant to correspond to roughly a one in a thousand chance of default within three years. That figure comes from corporate rating transition studies, because the sovereign sample is too small to measure properly. Which is itself the point: there are so few rated sovereigns, and so few defaults among the well-rated ones, that a single Greece breaks the statistics.

And Greece happened. Rated A in 2009, restructured in 2012, with private creditors taking a haircut of more than half in the largest sovereign restructuring in history. Russia was investment grade weeks before its 2022 default. And for anyone who finds those examples comfortably foreign: South Africa defaulted in September 1985, freezing $13.6bn of foreign debt in the standstill that followed the Rubicon speech, with the financial rand resurrected to trap capital at home. The final payment on that 1985 debt was made in 2001, sixteen years later.

The usual response is that a government borrowing in its own currency cannot default, because it can print. Russia defaulted on its rouble-denominated GKOs in 1998. Printing was available. Default happened anyway, because default is a political decision as much as an arithmetic one.

We still discount liabilities on the government curve, hold government bonds at a zero capital charge for spread and default risk, and call the whole arrangement risk free. For most purposes that is a reasonable working convention. It is worth remembering that it is a convention.

5. Regulation

Not just the rules changing, which we half expect and occasionally even get consulted on. The interpretation and application of regulation can shift while the words stay identical.

Foreseeable dividends under SAM is a live example. The prudential standard has not changed. But the expected treatment has moved from deducting dividends from own funds once declared and near certain, to accruing an allowance for the coming year’s dividends through the quarterly QRTs. The SCR is unchanged, own funds are lower, and the reported cover ratio drops, on the same standard, for the same insurer, with the same balance sheet. Europe has seen the same dynamic: EIOPA has revised its guidance on matters like contract boundaries without any change to the underlying Directive.

This kind of shift is difficult to explain to a board. The regulation stayed the same but the answer moved. It sits in almost nobody’s risk register, precisely because the words on the page look so solid, and it arrives through industry letters, technical observations and supervisory feedback rather than through anything a legal review would catch.

6. Which power runs the world

Yes, this one is obvious. Persians, Greeks, Romans, Ottomans, British. Everyone knows empires end.

But obvious is not the same as internalised. A Roman in CE 50 could not have imagined Rome falling, and Rome had another four centuries in the west, and fourteen in Constantinople, of not falling to prove them right. In 1913 Britain ruled the largest empire the world had seen, close to a quarter of the map and of its people, and did not expect it to be dismantled within 50 years. India was gone by 1947 and most of the African colonies by the mid-1960s. The Soviet Union was a superpower until 1991, and then, within months, not a country.

Reserve currency status has already changed hands within the span of a long life: sterling held the position the dollar holds now, and lost it over the middle decades of the twentieth century.

Today, with the dollar in every reserve, US capital markets in every portfolio and Silicon Valley in every pocket, it is hard to feel that any of it could end. That difficulty is the whole point. It is exactly what certainty felt like in Rome, in London, in Moscow. I have no prediction to offer about when or how American primacy ends, and I would distrust anyone who does. The exercise is not prediction. The exercise is noticing that “I cannot imagine it” describes the limits of my imagination, not the limits of the world.

7. Whether to give your kids peanut butter

A palate cleanser, and a serious one. For years the official advice was to avoid peanuts in infancy, especially for high-risk children. The American Academy of Pediatrics recommended avoidance until age three in 2000. That guidance was withdrawn in 2008, and then the LEAP study in 2015 showed the opposite of the original advice: early exposure cut peanut allergy in high-risk infants by just over 80%, and by 2017 the formal guidance recommended deliberate early introduction.

Settled science on feeding children, followed conscientiously by a generation of parents, turned out to be not just wrong but inverted. If that can flip, the confident consensus in your own field deserves at least an occasional raised eyebrow.

The pattern

The pattern is not that things change. Everyone knows things change, and a list of changes is just trivia. The pattern is that each change was unimaginable the day before, to serious people with good information, and “unimaginable” turned out to be a statement about their imagination rather than about the world.

For those of us who build and rely on models, this has a practical edge. “Certain” in a model usually means “we chose not to model it”. Tax rules, the peg, the government curve, the supervisory interpretation: each is a variable somebody decided to hold constant. Sometimes that is a sensible choice. Modelling everything is neither possible nor useful, and a model that treats everything as uncertain tells you nothing.

But it is a choice, and it should be a conscious one. A worthwhile exercise, for an ORSA or simply for an afternoon: write down the certainties your model assumes, pick the one that feels most absurd to question, and ask what would happen if it moved. The one that feels most absurd is the interesting one. That feeling is the tell.

Even death only makes the certain list on outcome, not timing. Modelling the gap between the two is most of what actuaries do.

What belongs at number 8?

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?

This article outlines a collection of common and problematic pitfalls I’ve seen in ORSA stress and scenario testing, capital modelling, and governance. Some are technical, some cultural, and all are worth addressing if we want the ORSA to do what it should: support better decision-making under uncertainty.

These insights reflect my experience across a wide range (and varying quality) of ORSAs, including independent reviews, informal and formal regulatory feedback (including from the Prudential Authority), informal discussions with regulators, and public statements from supervisors across multiple jurisdictions.

1. Toothless Scenarios and Soft Stresses

• Many ORSA scenarios are too mild to test anything meaningful.
• Often there’s no indication of severity. Is this a 1-in-5 or 1-in-50 event? Without context, interpretation is impossible.
• Firms are sometimes surprised they survive a 1-in-200 scenario, forgetting that survival at that level is by design.

2. Recycled, Stale, or Misaligned Scenarios (and Ignored Emerging Risks)

• Same tired stresses reused each year without meaningful refresh.
• Narrative scenarios assigned numerical calibrations that don’t match the story.
• Horizon scanning is often absent or perfunctory; emerging risks must be systematically identified and tested.
• Scenario testing should anticipate what could plausibly happen next, not merely repeat past events.

3. Implausible or Alienating Scenario Design

• Unrealistic or inconsistent scenarios alienate management and the board.
• Severe scenarios are valuable, but they must be framed with historical precedent or research to be credible.
• Overconfidence in models is dangerous; even the best models can fail catastrophically, as history shows.

4. Over-Engineering vs Usefulness

• Attempting to build the “most accurate” pandemic scenario misunderstands the point: scenarios are for learning and planning, not for prediction.
• Prioritise strategic insight over technical perfection.

5. Investment Returns Detached from Reality

• While not common, flat investment income across stress scenarios is a serious modelling failure.
• Investment returns must reflect changes in asset levels and market conditions under stress.

6. LACDT: Tax Calcs Behaving Badly

• Deferred tax recoverability often lacks robust testing.
• Future stressed profits must first create a DTA before any LACDT benefit can be recognised. Tiering here can hit you – more than you considered for the base SCR calc and your QRT.
• Income vs capital gains treatment and tax fund nuances are often overlooked.
• Just because LACDT can’t be negative (per the FSIs), doesn’t mean you can’t have existing DTAs fail recoverability testing in a stress and have loss amplification from deferred taxes!

7. Tiering and Fungibility Constraints Not Considered

• Capital tiering restrictions often ignored under stress.
• Assumed fungibility between entities or tiers can be unrealistic, especially under stress scenarios.
• See the point about DTA and tiering above too.

8. Over-Reliance on Standard Formula Extrapolation

• Normal distribution assumptions are often inappropriate; t-distributions, Lognormal, Pareto tails, or piecewise fittings are better suited.
• Ideally your own experience should be able to inform 1 in 10 stresses and act as a sanity check on scaled 1-in-200 stresses.
• Thin historical experience leads to poor calibration of rare-event risks, especially for equity markets.
• And really, there are several standard formula stresses that are probably not appropriate as a starting point. Some non-life cat stresses may be too conservative – and mass lapse has its critics, but life cat risk, expense risk, and retrenchment risk stresses are likely too low.

9. Unrealistic Business Volume and Expense Assumptions

• Base cases often adopt stretch targets as certain outcomes.
• Expenses are incorrectly assumed to scale perfectly down with policy volumes, ignoring the reality of fixed costs.

10. Incurred vs Paid Confusion

• Claims incurred and claims paid are routinely confused. The impact on profit vs balance sheet and cash can be counter-intuitve.
• Timing differences, especially under IFRS 17 (LCI/CIP dynamics), matter for liquidity and solvency modelling.

11. Short Projections for Long Risks

• Three-year horizons are insufficient for long-burn risks including the obvious candidate – climate change.
• Five years should be the baseline internally, with qualitative insights over longer horizons. Yes, the reliability decreases as the term increases, but it can still be informative. You may chose to disclose only 3 years more broadly, but the longer view is important to at least understand trends.
• Long-term (10–30 year) qualitative assessments should supplement the ORSA, accounting for amplifying systemic interactions.

12. Disconnect Between ORSA and Management Forecasts

• Management runs the business based on one view; the ORSA is prepared using another.
• Without alignment, the ORSA cannot pass the use test or add value to strategic decision-making.

13. Ignoring Dynamic Risk Interactions

• Risks are often modelled in isolation.
• In reality, correlations and feedback loops matter: lapse impacts guarantees, claims experience shifts reinsurance pricing, and market volatility affects lapse and claims simultaneously.

14. Either No Management Actions, or Superhero Versions

• Some ORSAs model no management actions (overly conservative but unrealistic).
• Others assume immediate, flawless actions without delay or cost (equally unrealistic).

15. Unexplained Profit and NAV Changes

• ORSA profit projections must reconcile to balance sheet movements.
• Adjustments between IFRS and SAM/Solvency II frameworks should be clearly documented.

16. ORSA Process Too Slow to Be Relevant

• A nine-month ORSA development cycle leads to stale outputs.
• ORSA timing must be aligned with the business planning cycle and responsive to external shocks.

17. Weak QA and Model Review

• Detailed, independent model review is often absent.
• Common failures include claims timing mismatches, unrealistic ROEs, omitted asset growth dynamics, and unstated assumption interactions.

18. Boilerplate Overload, Insight Underload

• ORSAs are often bloated with standard wording, burying the important insights.
• Focus must remain on what is changing and what genuinely informs management decisions.

19. No Trigger or Process for Out-of-Cycle ORSA

• Firms sometimes only trigger an out-of-cycle (OOC) ORSAs for an SCR breach — far too late. If the risk or solvency situation (internal or external) has changed, it’s time for an OOC.
• Proportional, trigger-based OOC ORSAs must be defined and actioned when material changes occur.
• An OOC doesn’t need to cover the entire process or the full 80 page report. Just the key parts that have changed.

20. Reverse Stress Testing as an Afterthought

• Reverse stress testing needs to explore genuinely different failure modes, not just ramp up severity.
• Defining what constitutes “failure” (capital breach, strategic collapse, or profitability death spiral) needs careful thought.

21. Weak or Missing Rationale for Scenario Selection

• Documenting why scenarios are chosen reveals how the firm prioritises risk.
• Disconnects between identified risks and tested scenarios highlight critical weaknesses.

22. Board Engagement and Use Test Failures

• Board sign-off without meaningful engagement misses the point.
• Effective risk functions bring ORSA components to the Board repeatedly during the year to drive strategic debate.

In Closing

None of these issues is inevitable. Most stem from habits — some from lack of scrutiny, others from good intentions that weren’t tested hard enough. But if the ORSA is to support real-world resilience, it has to reflect how capital and risk actually behave. That means grounding assumptions, engaging the business, and constantly asking: “Would I act on this?†

If the answer is no, the ORSA needs work. If the answer is yes, you’re on the right track.

Hyperinflation, Currency Crises & Insurance Industry Collapse

Hyperinflation destroyed Zimbabwe’s insurance sector, and decades later, it still hasn’t recovered. Currency crises in Lebanon, Argentina, and Venezuela have crippled financial institutions, showing how fragile financial systems can be when trust in money itself disappears.

A recent discussion started as a tongue-in-cheek debate: Is inflation a more efficient way to raise revenue than taxation? But it evolved into a broader debate on monetary risk, Bitcoin, inflation, and long-term economic trends—and why so many common arguments deserve scrutiny.

How Inflation Impacts Insurance

Premiums & Inflation Risk
High inflation makes level premiums unworkable, erodes the real value of cover. Optional benefit increases create adverse selection problems in life insurance. Even constant percentage increases fail under volatile inflation, and real wage stagnation worsens affordability pressures.

Monetary Instability & Insurer Solvency
Currency collapses create huge challenges for insurers trying to meet liability obligations in real terms. When inflation spikes, reserves built on past assumptions become grossly inadequate, leading to solvency concerns and even industry-wide failure.

Crypto & Smart Contracts in Insurance
Blockchain has potential for parametric insurance, automated claims processing, and fraud reduction. But much of the excitement outpaces practical application—or solves problems that were already solved while not addressing key remaining challenges.
(And let’s be real—just because a smart contract auto-executes doesn’t mean lawyers won’t find ways to argue intent and “meeting of minds.†)

My (Cautious) View on Blockchain

I spoke at the 2016 ASSA Convention on Seductions of the Blockchain, and my position remains:

• Cautiously optimistic
• Interested in opportunities
• Frustrated by the lack of rigorous debate from both fanatics and skeptics

The fanboys see blockchain as a cure-all, while the status-quo-invested skeptics dismiss it entirely. Reality, as always, is more nuanced.

Key Arguments & Concerns

1 Inflation as an ‘Efficient’ Tax?

Some argue that taxes are administratively complex, difficult to collect, and inflation acts as an “invisible tax† that transfers wealth to the state with less friction.

The Problem? Inflation isn’t a neutral mechanism:

• Distorts price signals and makes long-term contracts unreliable.
• Increases uncertainty and raises borrowing costs.
• Disproportionately harms those without inflation-protected assets—often the poorest.
• Erodes trust in government’s ability to manage financial stability.

Hyperinflation isn’t just caused by overspending—it requires excessive money printing to cover deficits. Many governments (e.g., Japan, the US, and EU countries) have run huge deficits for years without hyperinflation because they borrow responsibly instead of monetising debt.

Friedman’s famous quote:
“Inflation is always and everywhere a monetary phenomenon in the sense that it cannot occur without a more rapid increase in the quantity of money than in output.”

2 Bitcoin as a Predictable Alternative to Fiat?

Bitcoin proponents argue that a fixed supply prevents inflation and provides monetary certainty. But there’s a flip side:

The Problem? A rigid money supply is deflationary, which discourages spending and investment:

• BTC expansion (~0.9% today, falling below 0.5%) is well below global population and economic growth.
• Fixed-supply currencies have historically failed because economies need monetary flexibility to adjust to shocks.
• A deflationary currency discourages productive investment. If BTC’s price is expected to rise, why spend it? Why take out a loan?

This is why almost all mainstream economists—from Keynesians to monetarists—support some level of controlled monetary expansion.

Academic reference: Friedman advocated rules-based money supply growth, not a hard cap. Even Hayek, a proponent of free-market money, acknowledged the need for adaptable monetary systems.

A bigger issue: Some crypto coins have fixed supply, but the total universe of crypto coins is unlimited. New projects, forks, and tokens emerge constantly, meaning there is no true scarcity at a system-wide level.

3 Credit Risk & Smart Contracts – Who Pays When the Funds Aren’t There?

Smart contracts don’t solve credit risk. Traditional insurers must hold capital reserves and meet solvency requirements to ensure claims can be paid. Smart contract-based insurance lacks an equivalent safety net—yet.

Key Risks:

• No Guarantee of Payouts: If a smart contract is underfunded, it can’t issue emergency capital or negotiate claims—it just fails.
• Over-Collateralization Isn’t a Perfect Fix: Many DeFi protocols require excessive collateral to mitigate risk, but this limits scalability and locks up capital inefficiently. Actuarial approaches to capital adequacy could provide a smarter balance.
• Cascading Failures in Market Shocks: A major market downturn can cause mass liquidations, leading to systemic failures—just like traditional financial crises, but with fewer stabilizers.

Future Opportunity:

• As DeFi regulation increases, some form of capital adequacy requirements (like Solvency II for insurers) may emerge.
• Actuaries and insurance risk experts could play a role in designing smarter DeFi risk models.

Final Thought: Smart Contracts Are an Exciting Tool—but They Need More Work

Smart contracts introduce new efficiencies, but they also introduce new risks:
They remove intermediaries—but also eliminate safety nets.
They change fraud risk—but introduce oracle manipulation risk.
They enable fast, automated transactions—but don’t guarantee funds will always be there when needed.

For insurance, finance, and risk management, blind reliance on smart contracts is dangerous. But recent advancements show promise:

• Regulators are starting to provide legal clarity.
• Hybrid smart contracts (automated + human oversight) are emerging.
• Decentralized oracles & improved collateral models are evolving.

The real opportunity? Combining smart contract automation with actuarial risk management principles to build more resilient decentralized insurance solutions.

Would love to discuss with those working in insurance, risk management, DeFi, and blockchain regulation.

#Inflation #Blockchain #BTC #ETH #DeFi #DistributedLedger #MonetaryPolicy #FinancialRisk #Insurance #RiskManagement #Actuary #Economics #LegalRisk #ParametricInsurance

The data is compelling: global population will likely peak sometime this century before beginning a sustained decline – a phenomenon unprecedented in modern history.

The Emerging Demographic Reality

The charts from the Global Aging Institute tell a compelling story. Most demographic models now predict global population peaking between 2064 and 2086, with maximum populations ranging from 9.7 to 10.3 billion people. What’s particularly notable is that newer projections tend to forecast earlier and lower peaks than older ones – suggesting that fertility decline is accelerating beyond previous expectations.

China represents perhaps the most dramatic example of this demographic shift. Once feared for its population explosion (internally and externally, but perhaps for different reasons), China’s fertility rate has plummeted to approximately 1.2 children per woman – far below the replacement rate of 2.1. China’s population peaked in 2020. The precipitous decline in fertility has already resulted in a decline in the population. Some models now suggesting its population could halve (or worse) by 2100 from its peak.

The Middle Income Trap and Demographic Headwinds

This chart illustrates what demographers call the “middle income trap,” where countries achieve middle-income status but struggle to join the ranks of high-income nations. Despite substantial growth in East Asia (6.4% annually) and South Asia (3.9% annually) between 1990 and 2022, their GDP per capita remains far below U.S. levels. Meanwhile, regions with lower growth rates like Middle East & North Africa, Latin America, and especially Sub-Saharan Africa (0.8%) show little convergence with developed economies. Sub-Saharan Africa has become relatively poorer relative to the US in the last 30 years.

A key insight here is that population dynamics may exacerbate this trap. Many middle-income countries are ageing rapidly before achieving high-income status – a phenomenon economists call “getting old before getting rich.” Sub-Saharan Africa as a region is almost unique in that it is still growing. But this rate is declining and the global pattern is clear.

This creates a challenging environment where countries must support ageing populations without the institutional and financial infrastructure that developed economies built during their demographic dividends.

Immigration: The Decisive Variable

This chart highlights immigration’s critical role in determining population trajectories in low-fertility environments. For the United States, the difference between the “zero immigration” and “high immigration” scenarios by 2100 is stark – 226 million versus 435 million people. This 209 million person difference exceeds the entire current U.S. population.

This reality transforms immigration from a purely social or political issue into a fundamental economic consideration. Countries with below-replacement fertility essentially face a choice: accept immigration or manage decline. Japan has largely chosen the latter path, while countries like Canada and Australia have embraced the former. The economic implications of these choices will shape national fortunes for decades.

Beyond GDP Growth: Rethinking Economic Impact

A recent conversation with a colleague raised an important question: Does population decline necessarily mean economic weakness? This requires nuanced analysis beyond simple GDP growth metrics.

The Debt Challenge

Population decline creates particular challenges for debt sustainability. With slower or negative population growth, overall GDP growth becomes more dependent on productivity improvements. This makes debt/GDP ratios harder to reduce through growth alone, potentially forcing difficult fiscal adjustments. While automation and technological advancement could boost productivity to offset population decline, experience suggests achieving sufficient productivity growth consistently is challenging.

Labor Market Transformations

An ageing, shrinking population dramatically alters labour market dynamics. While labour shortages may drive wage increases in certain sectors, they can also accelerate automation and reshape entire industries. Japan’s response to its demographic challenges provides valuable lessons, with its emphasis on robotics and technology reflecting adaptation rather than surrender to demographic destiny.

Consumption Patterns and Capital Markets

The traditional life-cycle theory of consumption suggests peak spending occurs in one’s 30s and 40s before declining in later years. However, emerging evidence indicates that aging societies develop different consumption patterns rather than simply reduced consumption. Healthcare, leisure, and personalized services become more prominent, while housing and transportation may decrease in importance.

For capital markets, the traditional view suggests an “asset meltdown” scenario as retirees liquidate investments. Yet the evidence for this remains mixed, with capital flows, policy interventions, and changing retirement patterns creating more complex outcomes than simple models predict.

Insurance Industry Implications

As an actuary, I see several potential implications for insurance markets in this demographically transformed landscape:

Long-Term Care Evolution

Increasing longevity combined with smaller families creates greater need for formal long-term care insurance, though significant challenges remain:

• Greater prevalence of LTC outside the US: As family sizes shrink, dependent ratios increase, and individuals live longer in retirement, Long Term Care (LTC) becomes an increasingly key product. LTC has had a difficult few decades in the US. It’s still fairly uncommon in emerging markets and even some developed markets. Early adopters in these markets can establish the brand, credibility, and experience to become a major player in this market as it grows.
• Hybrid Products: Traditional standalone LTC policies may give way to life/LTC or annuity/LTC hybrids that address the “use it or lose it” concern that has limited market acceptance.
• Home Care Focus: Products emphasizing aging-in-place technology and home care services rather than institutional care will likely gain prominence as consumer preferences shift.
• Public-Private Partnerships: The scale of the long-term care challenge may necessitate government involvement, with insurers potentially managing supplemental coverage above a public baseline.

Retirement Income Transformation

The traditional accumulation-to-decumulation retirement paradigm faces fundamental challenges:

• Flexible Drawdown Solutions: Products will need to accommodate phased retirement, part-time work, and variable income needs over potentially 30+ year retirement periods.
• Longevity Insurance: Advanced-age annuities that begin payments at 80 or 85 may become more prevalent as longevity risk pooling becomes essential for sustainable retirement planning.
• Integration with Healthcare: Retirement products that explicitly address healthcare cost uncertainty will become increasingly important, potentially with features that adjust income based on health status changes.

Investment and Risk Management Innovation

Traditional asset allocation approaches require rethinking:

• Extended Risk Horizons: Longer retirement periods necessitate maintaining higher equity allocations later in life, challenging conventional glidepath models.
• Real Asset Focus: Inflation protection becomes more critical with extended retirement periods, potentially increasing demand for real estate, infrastructure, and inflation-linked securities.
• Intergenerational Products: Multi-generational wealth transfer solutions that optimize across family units rather than individuals may emerge as family structures adapt to longevity.

Industry Structure Changes

Demographic shifts will reshape the insurance landscape structurally:

• Consolidation Pressure: Declining population in certain markets will reduce the absolute size of insurance pools, driving consolidation as fixed costs must be spread across smaller customer bases.
• Digital Transformation: Cost pressures will accelerate automation in underwriting, claims, and customer service, potentially turning insurance from a high-touch to a primarily digital industry.
• Scale vs. Specialisation: Large multinational insurers with the scale to invest in technology may have advantages, while specialized insurers focusing on specific demographic niches could also thrive.

Geographical Divergence

Insurance markets will increasingly bifurcate:

• Mature Markets: Rapidly aging countries will prioritize decumulation solutions, long-term care, and longevity protection.
• Growth Markets: Countries still experiencing demographic dividends will focus on protection, accumulation, and developing institutional capabilities.
• Cross-Border Opportunities: Insurers able to transfer knowledge between these divergent markets may develop competitive advantages through global learnings.

Addressing Behavioral Challenges

Demographic changes intensify existing behavioral biases:

• Longevity Underestimation: Products will need to address systematic underestimation of lifespan by consumers, potentially through novel framing of longevity risk.
• Cognitive Decline Protection: Financial products incorporating protection against diminished financial capacity in advanced age will become increasingly important.
• Family System Integration: Insurance solutions recognizing the role of family systems in later-life care and financial management will gain prominence.

Alternative Perspectives: Not All Decline

While I’ve outlined several challenges, it’s important to consider alternative viewpoints that paint a more optimistic picture of demographic change:

Productivity Growth as Compensation

As referenced earlier, technological advancement and artificial intelligence could potentially drive unprecedented productivity growth that more than offsets population decline. Countries like South Korea have maintained strong economic performance despite rapidly falling birth rates. The labor scarcity created by shrinking populations might accelerate automation and AI adoption, potentially unleashing productivity increases that our models currently underestimate.

Per Capita Prosperity vs. Total GDP

While total GDP might grow more slowly in shrinking populations, GDP per capita could still rise. Fewer people sharing national resources might lead to higher individual living standards, particularly if automation effectively addresses labour shortages. However, this doesn’t fully address debt sustainability issues – a challenge that might create incentives for moderate inflation to decrease the real value of accumulated public debt. Supply constraints from declining labour forces could contribute to such inflationary pressures.

It’s also worth questioning whether GDP per capita is even the right measure for societal wellbeing in aging societies. This metric excludes non-monetary aspects of wellbeing and non-remunerated work. An over-focus on GDP might misrepresent a future that includes many content retirees engaged in meaningful but economically unmeasured activities – from community service to artistic pursuits.

Environmental Benefits

A significant but often overlooked benefit of population stabilization or decline is reduced environmental pressure. Lower population could ease resource competition, reduce pollution, and potentially support more sustainable economic models. While climate change rightfully dominates environmental discussions, population stabilization represents one of the most effective (if slow-acting) approaches to reducing humanity’s ecological footprint.

The Japan Question

Japan’s experience with population decline provides a complex but instructive case study. Despite demographic headwinds, Japan has maintained relatively high living standards, low unemployment, and social stability. Their example suggests adaptation is possible, though not without trade-offs. Japan’s emphasis on automation, careful immigration, and social cohesion offers one path for other ageing societies.

Global vs. Local Asset Markets

While individual countries might mitigate asset price declines through global capital flows, the prospect of worldwide population aging raises questions about whether these balancing mechanisms will remain effective when most major economies face similar demographic trajectories simultaneously. This remains one of the great unanswered questions in demographic economics.

Adaptation Over Decline

Despite these challenges and opportunities, history teaches us that economies adapt. The 1950s comparison my colleague raised is instructive – economic structures reorganize around demographic realities. The key difference today is the direction of change: we’re entering an era where labor becomes more scarce rather than more plentiful.

This transition creates winners and losers. Countries and companies that successfully adapt to aging populations through technology, immigration, and institutional innovation will thrive. Those clinging to growth models predicated on expanding populations may struggle.

For actuaries and financial professionals, these demographic shifts demand fresh thinking about longevity risk, retirement adequacy, and intergenerational equity. Our traditional models built during an era of population growth require fundamental reconsideration.

The future may not be one of economic decline, but rather one of economic transformation – driven by demographic forces that are now firmly established and unlikely to reverse in the coming decades.

Some sobering findings:

• For conflicts with >10M casualties, average waiting time is 136 years
• The “Long Peace” of 70 years isn’t statistically significant
• Historical data actually underestimates true violence by at least half
• Under fat-tailed distributions, the mean is dominated by extreme events

Most striking? They quote an 1860 assessment celebrating an “unprecedented” 40-year peace and declining warfare… right before the bloodiest century in human history.

With current tensions in Europe, Middle East, US-China relations, and emerging climate pressures, are those saying “Humanity has changed! This time is different!” the same mistake of extrapolating recent stability?

Statistical humility matters: For fat-tailed events, we need much more data to claim peace than to claim risk.

• Growing regulatory acceptance as parametric solutions prove their value. (Issues of insurable interest have posed problems. Currently in testing in “sandbox” regulatory environments in a few countries including South Africa, where it has traditionally been viewed as non-compliant.)
• Addresses previously uninsurable risks for corporates and governments, filling protection gaps. Good application for captive insurers (I’ll cover this more in a later post)
• Complements reinsurance by covering areas traditional policies often exclude
• Primarily used for commercial lines, but personal applications are emerging
• Significant applications for transferring country-level risk for governments and certain NGOs
• Basis risk remains a consideration, but can be mitigated somewhat through careful structuring

Exciting developments include parametric ETFs, allowing investors to participate in this innovative market. We’re also seeing creative applications using new data sources, like phone signals to assess footfall.

I can get theoretically excited about smart-contracts for parametric insurance, but in practice this quickly feels like unnecessary complexity with limited current benefit.

Parametric insurance can compete with reinsurance, but it’s often best used in combination, or as a tool for reinsurers to spread risk

As always, professional advice is crucial when exploring these solutions.

🔬 PFAS linked to health risks

⚖️ Regulatory scrutiny increasing

💼 D&O and liability cover at risk

🌍 Exposure is everywhere

💰 Costs could be astronomical

Liability insurers have a growing emerging risk relating to the potential wave of lawsuits related to PFAS (per- and polyfluoroalkyl substances, or “forever chemicals”) which have been linked to serious health problems and are now the subject of increased regulatory scrutiny and legal action.

Recent developments, such as the US EPA’s decision to regulate PFAS in drinking water and designate two PFAS chemicals as hazardous substances, are expected to trigger a surge in litigation. Water utilities, local communities, and others may seek compensation for cleanup costs, shifting the financial burden to the polluters.

https://www.nytimes.com/2024/05/28/climate/pfas-forever-chemicals-industry-lawsuits.html

The scope of potential liability is vast, with some experts comparing the scale of PFAS litigation to that of tobacco, asbestos, and MTBE combined. Unlike those cases, however, PFAS exposure is widespread, affecting nearly every person in the US and developed markets, and quite likely in South Africa too.

But the risks extend beyond product liability. As we’ve seen with tobacco and the growing litigation risk from climate change, PFAS could also significantly impact D&O cover. Directors and officers of companies that use or produce PFAS could face claims alleging failure to disclose risks, mismanagement of PFAS-related issues, or even derivative lawsuits from shareholders arguing that their actions (or inaction) have harmed the company’s reputation, financial performance, or legal standing.

Pretty much every one of us is walking around with PFAS in our bodies. And we’re being exposed without our knowledge or consent, often by industries that knew how dangerous the chemicals were, and failed to disclose that.

Insurers should proactively assess their exposure, review policy language, and engage with policyholders to mitigate risks where possible.

While the full extent of PFAS litigation remains to be seen, it’s clear that the potential costs could be substantial.

Climate and cyber aren’t the only emerging risks.