Risk – We Know Zero

Preparing to Fail

27. October 2026 · Write a comment · Categories: Birmingham, Cartoons, Health, Media & Politics, Risk

I developed a business continuity plan for the Birmingham office I worked in once. It involved a mainly paper exercise, establishing where the various links required were, having a gawp at the spare office space available to us somewhere near Leicester if the shit went down, but also preparing a bag containing torches and a copy of our plan. I think we might even have sprung for a hi-viz jacket.

Now apparently we are all suddenly expected to do something similar on an individual basis. In the face of risks unspecified, we have been told that 3 in fact really IS The Magic Number. Food and water for three days and nights, by which time presumably it will be safe to venture outside (!). Fresh from the success of See It – Say It – Sorted we now have three phrases depending upon whether we are running into the house (Go In – Stay In – Tune In) or out of the house (Get Out – Stay Out – Ring 999). We are all encouraged to fill out a Household Emergency Plan. This includes space to fill in the number of your GP and insurance company details, like you might do in the aftermath of an unsatisfactory holiday. Also it is suggested you note down the FM frequency of radio stations you would turn to for national or local news, like when you need to know whether schools are closed. All couched in familiar terms so as, presumably, not to frighten the horses.

By failing to prepare you are preparing to fail, as Benjamin Franklin almost certainly did not say.

Well, neigh.

It all starts to go tits up when you look at your local area’s resilience forum. Here’s mine:

They have a range of advice across these risks:

I went straight to the Power failure one, as there seemed to be more attention given to this risk than any other in the emergency supplies section of my National Emergency Plan:
● Torch (battery/wind up) and spare batteries – torches are safer then candles
● FM radio (battery/wind up) to get updates during a power cut
● Portable power bank to charge phone and medical devices (if compatible)

One of the 5 headline pieces of advice under Power failure is, hilariously, “Consider an analogue landline phone that will work in a power outage”.

No it won’t. Apparently they have not been keeping up to date with the activities of openreach, whose website cheerfully lays out the sell by date for this particular piece of advice:

It is not the only problem with the information provided by West Midlands Resilience Forum. Their list of organisations to turn to in a crisis includes integrated care boards which exist in name only in the current chaos at the top of the NHS and the wrong name for the region’s main airport, amongst the issues I could immediately spot:

So it is tempting to perhaps suggest that they update their own national emergency plan before demanding them from us. Particularly as Wes Streeting’s contributions appear to have pushed up sales of tinned goods at Iceland by 40% over the weekend, thereby making gaps on the shelves more likely for everyone else.

Remember Raymond Briggs’ parody of the government’s Protect and Survive pamphlet from the mid 70s – supposedly allowing us to individually prepare for nuclear war – When The Wind Blows? It feels like we are right back there:

And yet. Carol Cadwalladr has a different take:

A confession: I’ve been a covert prepper since a conversation with an Irish open-source researcher, Gavin Sheridan, two years ago. It was at the time Russian spy ships that were circling Britain and Ireland and the anchor of a shadow fleet tanker had “accidentally” severed a cable between Finland and Estonia.

What’s the worst thing that could happen if they did succeed in cutting the cables between Britain and America, I asked Gavin. He thought for a moment “Well, I guess the first thing would happen would be the total collapse of the global economy,” he said cheerfully. I told him I was thinking of buying a wind-up radio. I’d contemplated the idea of not knowing what was happening but at the same time I didn’t want to become one of those people US cable networks make documentaries about.

“I’ve got two!” Gavin said. “One for me and one for my parents.” When I saw him this summer he told me that he’d now got a Starlink antenna stashed away in his cupboard. Another of my friends, a world-renowned European tech expert, told me a year and a half ago that she keeps cash in the house and the tank of her car full. I would never have admitted publicly that I followed suit with the cash or that after European intelligence chiefs warned of an imminent attack by Russia this September, I got some water containers in too. It would have been much too embarrassing. And yet here we are with an extensive list the UK government has just put out – blankets, torches, three days’ supply of food, a power bank – it makes my own efforts look frankly pathetic.

There are risks out there. And most other European countries gave their citizens similar advice several years ago.

Then there is Cory Doctorow on the risk of the United States deciding to “brick” essential goods and services because we have done something Trump doesn’t like:

Now that Trump has made it clear that the US no longer has allies or trading partners, only rivals and adversaries, everyone in the world is trying to figure out whether they can trust American tech infrastructure with their governments, businesses and personal data.

The answer is a resounding “no”. Just look at the international criminal court, which ditched Microsoft Office for a European alternative after Trump sanctioned its officials for issuing an arrest warrant for the génocidaire Benjamin Netanyahu. Directly after Trump denounced the court, its justices lost access to all their Microsoft accounts – emails, documents, calendar, address book. The court was, in effect, “bricked”. Microsoft denies this, but between the justices of the international criminal court and the US tech monopolist, I know who I trust.

Trump is capable of weaponising the US’s tech companies, and there’s no telling where it will end. Remember that when Russian looters stole millions of dollars’ worth of John Deere tractors and spirited them away to Chechnya, the company was able to push a kill signal to the tractors that rendered them inoperable.

Until we repeal the anti-circumvention law, we can’t reverse-engineer the US’s cloud software, whether it’s a database, a word processor or a tractor, in order to swap out proprietary, American code for robust, open, auditable alternatives that will safeguard our digital sovereignty. The same goes for any technology tethered to servers operated by any government that might have interests adverse to ours – say, the solar inverters and batteries we buy from China.

And we also know from Paul Behrens’ contribution to the National Emergency Briefing that the UK is “woefully unprepared” for food system failure.

So some level of individual preparation, while certainly nowhere near sufficient, may perhaps be necessary. And here the West Midlands Resilience Forum are quite right that all of us who are able to need to “reduce the need for support from others and enable you to support the vulnerable members of your street and community”.

Now where did I put that torch?

10 Years Ago: Actuaries and the New Manhattan Project

20. October 2026 · Write a comment · Categories: Actuarial, AI, Professions, Regulation, Risk

*Source: Universal Pictures Publicity, Fair use, https://en.wikipedia.org/w/index.php?curid=71354716*

10 years ago I was working my way through Cathy O’Neill’s excellent Weapons of Math Destruction, the first really loud warning about the inappropiate application of algorithms, and blogging about it. In the book she set out the case against devolving important decisions to mathematical models without adequate feedback loops. Yes, 10 years ago!

I concluded that post by paraphrasing Robert Oppenheimer’s words about the Manhattan Project:

If mathematical models are to be the dominant regulatory tool of a financial world, and of the consultancies and financial firms competing in that world, then the time will come when mankind will curse the names of the highly paid professionals who followed inappropriate rules rather than exercising their own expert judgement when it mattered.

I think that is still a work in progress. Although at least they have made a film about the Manhattan Project in the meantime. And you could say the United States certainly appear to be embracing the analogy:

Meanwhile Cathy O’Neill has done a lot more than just wring her hands about these risks. She has set up her own consultancy (O’Neil Risk Consulting & Algorithmic Auditing or ORCAA) to audit your algorithms for you. As she says:

Whether it’s a hiring algorithm, healthcare AI, predictive scoring system, or generative AI platform, we are here to think about how it could fail, for whom, and what you can do to monitor and mitigate these risks. We are actively developing frameworks and governance approaches to help companies and organizations use algorithms and AI safely – confirming these technologies perform as intended and operate within sensible guardrails. We help our clients realize the transformative benefits of AI, while avoiding discrimination, bias, and other problems.

Their offering includes:

Algorithmic audit, including generative AI, automated decision systems, predictive models, and facial recognition;
Quantitative testing for regulatory compliance;
AI Governance and Risk Management Consultation;
What they call “Cockpit design” – “To make an effective cockpit, you must understand what can go wrong in flight, and include dials/gauges that monitor for those risks. We help build cockpits for your AI systems”; and
Education and Training.

ORCAA have been going for 7 years now, with a range of high profile clients, including AirBnB, Siemens and the City of Amsterdam.

This seems to me a good example of expertise being used in the right way.

Actuaries on the side of capital

16. October 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Climate Change, Economics, Education, Pensions, Professions, Risk

I think I may have been naive.

A couple of weeks ago I took the IFoA’s AI Manifesto to task for wanting to be on the Red Team rather than the Blue Team, ie the one supposed to be stopping as many AI errors as possible. As I said then:

The Red Team breaks into systems by exploiting a single vulnerability. That is comparatively easy. The Blue Team needs to reassure its clients that it has removed ALL vulnerabilities.

But what if you are not working to protect people from vulnerabilities? What if you are working to protect and accumulate capital instead?

Today I was alerted to the latest article from the IFoA GenAI Working Party, Agent Skills in the Enterprise: Making Actuarial Process Knowledge Reusable. It explains “how an agent skill package can connect reusable instructions with scripts, controlled references, templates, examples and tests”. This will allow organisations to “preserve not only completed models, reports and workpapers, but also the process checks, review decisions and handling of exceptions that produced them using the “agent skill” framework”. As the report puts it:

The problem is not simply that organisations need better prompts. They are preserving the outputs of professional work while leaving much of the procedure that produced them implicit. A completed report records what was concluded. It rarely records every data check, exception, escalation point and review decision needed to reach the conclusion reliably next time.

I can immediately see the motivation. I have been that person trying to replicate a previous valuation result produced by a former colleague I can’t contact, with incomplete data and a-no-longer-supported valuation system which keeps going on the blink. But it was the mention of process knowledge which caught my attention.

I wrote about process knowledge and its importance around a year ago, citing the example of the shrines at Ise Jingu in Japan (which I had learned about from Dan Wang’s Breakneck). This is a now over 1,300 year project to preserve the traditional techniques of building these shrines which involve no nails, just dowels and wood joints. Skills are passed from one generation to the next, there is a tree planting programme which has survived a dozen or more historical eras, pandemics, volcanic eruptions, earthquakes, invasions and oh so many wars over that period. As well as two nuclear bombs.

The important thing is not the ability to build a shrine over 1,300 years later exactly as it was built then of course. It is a proof of concept. It has preserved intricate difficult skills over more than a millenium. And it has involved people, lots and lots of people.

By contrast, the IFoA project has a different, rather shorter term, goal, which is laid out as follows:

This is the first of five articles. The second examines how completed work can become reusable packages. The third introduces SkillOps as the discipline for governing agent skill packages in actuarial work. The fourth considers the consequences for consulting when expertise previously carried through human capital is codified in reusable agent skill packages and becomes intellectual property. The fifth places skills within the wider challenge of continual learning.

It is the fourth article which really bothers me. Once not just the details of a project, but all of the other parts which do not normally get passed on (ie the process knowledge) are captured in this system, it becomes intellectual property, which Cory Doctorow defines as:

any rule, law or policy that allows a company to exert control over its critics, competitors or customers

Up until now, process knowledge has not been given much attention, mainly because it can’t be turned into intellectual property. Process knowledge has always been what remains in the heads of the skilled technicians employed by businesses after everything not nailed down which can be taken ownership of by those businesses has been. Process knowledge has always been the nailed down part. It is the part that has allowed the last vestiges of worker power within most corporations to survive.

Now a group of actuaries think they have come up with a plan to monetize that for the firm as well. To pull up those last remaining nails of worker power.

Think how the IFoA GenAI Working Party would have approached the Ise Jingu shrines.

First you outsource the tree planting programme to a specialist forestry manager. Minimum tender of course: need to get maximum value for money here. Don’t worry about having control over the delivery of timber of just the right type in just the right condition for a unique project. Just leave that to the specialists working on a budget.

Then you sit with the old guys taking you through the various processes involved. Might need some video capture for some of it. Make sure everything they say is captured and then cross-referenced for easier access subsequently. Don’t take too long over it as time is money. Don’t actually feel what it’s like to work with the tools, just note down everything about tool selection. Don’t actually apply the required pressure in the right direction with respect to the grain of the wood, its dryness, the precise cut from the trees selected. Just make sure the video is high enough quality.

I would be surprised if this project survived 20 years. In fact, in view of the fantasy economics of the AI industry, I would be surprised if it lasted even five years if the AI Agents involved in this project relied to any degree on the survival of any of the Magnificent 7.

And this is just to build a shrine out of wood.

These two projects are doing two different things. One is trying to preserve a human activity over a non-trivial period of time. The other is attempting to monetize it so that it can be included in the bottom line for the next quarter. And perhaps to float the idea that your business might be able to dispense with those pesky new employees acquiring process knowledge.

Which brings me to another alarm call. This time from an anonymous group of senior professionals in the UK and European insurance and reinsurance industries called Sounding the Alarm and published in English, French and German. It is not long, so I urge you to read it, but I want to focus on this passage about how the insurance industry is responding to the climate crisis:

Our industry’s strategy is to manage renewals in order to limit our exposure as the physical risk manifests so that premiums are sufficient to cover the rising claims load. This is not a malicious act but a simple response to the commercial realities we are faced with.

We will do this in three ways which many clients are unaware of and governments across Europe are failing to internalise the consequences of:

Firstly, we will reprice our cover. We will simply make policies much more expensive to reflect this elevated physical risk and uncertainty. This will have wider consequences for economic inequality, making it harder for less affluent households to access insurance. It will also increase costs for critical industries such as farming or shipping , creating a significant social and financial burden for government s and, in turn, its taxpayers.

Secondly, we will limit coverage to exclude elevated risks such as wildfires as well as other less predictable risks . The nature of insurance being re – negotiated on a 12 – month basis allows us to do this without friction. Ensuring customers really understand that this is happening is critical.

Thirdly, if there is no way to bring premiums and claims into balance or introduce risk reduction measures we will – for certain geographies and sectors – simply withdraw coverage . This will particularly affect coastal, mountain and flood-prone communities.

This approach is our way of managing our own financial exposure and maintaining profitability at a time of increasing physical risk and increasing uncertainty. Doing so is critical to ensure we can continue to pay claims, therefore fulfilling both our fiduciary duty and our role in society.

This is the flipside of the capitalisation of process knowledge of course. If it can no longer be monetized then ditch it, all the while spouting about “our role in society”. The people on the receiving end will not care whether it is a malicious act or not, it will feel like one.

Both of these stories tell us which side many actuaries are on, and it is the side of capital rather than the side of the people from who it has been extracted. It is not malicious, and it is how many actuaries understand fiduciary duty. To the contract rather than to public interest.

I look back on my own actuarial career. Some time as a scheme actuary where I really felt I was safeguarding the pensions of ordinary people, some time protecting the wealth of already fairly wealthy people from the tax authorities, some time in insurance feeling really quite remote from customers of any kind. And then 10 years helping students into actuarial work. Have I really just been a tool of capital this whole time? Is this why a IFoA sustainability WhatsApp group, which is all about connecting sustainability thought leaders together and raising awareness of the impact of the climate emergency across the finance sector (where I first saw the Sounding the Alarm memo), is so small?

Dan Davies, highlighting Robin Wriggleworth’s new book, A Fabulous Debt: The Epic Story of How Bonds Built the Modern World, wonders whether life insurance actuaries and general insurance actuaries (referred to as casualty actuaries here) have a similar relationship as bond traders and equity traders. It’s a good read. There is even a reference to Craig Turnbull’s magisterial A History of British Actuarial Thought! And this is the conclusion:

Which leads me to the paradox of the title. Reading the histories of actuaries, there seems to be quite a similar needle between the life and non-life insurers; one group place their trust in the higher mathematics, the other deals with heuristics, rules of thumb and vibes. It all comes to a head when you have to draw up the accounts, of course – the big intellectual battle seems to have been the casualty actuaries’ practice of setting reserves depending on the sum of each individual policy, which is really contrary to the spirit of treating risks as draws from a single distribution.

So it makes me think that there is some potential general principle here. Fields of human endeavour where management makes most use of complicated mathematics are likely to be less intrinsically complex and difficult. We use quantitative techniques only when we are content with regarding something as quantifiable.

This feels like a possible explanation. If your acknowledged expertise is in clever mathematics, you are likely to favour techniques which make more things quantifiable and therefore manipulable with clever mathematics. And the more actuaries flood into new fields, the further the quantifying will go.

Capitalism has always been in the business of quantifying things, allocating them to someone (often using considerable violence to do so) and then selling them as property. But late-stage capitalism has a now almost cartoonish obsession with quantifying even things that cannot be easily or successfully quantified, extracting ownership of the thing at the point of quantifying it and then selling it on.

When all the process knowledge has been turned into intellectual property, proprietary knowledge behind paywalls that hide the fact that it doesn’t really work any more, businesses will start to realise that they still have important gaps that are now nearly impossible to fill. There may be a Sounding the Alarm memo about it from anonymous insiders.

But it will be too late by then.

Telling a different story

07. October 2026 · Write a comment · Categories: Actuarial, Cartoons, COVID-19, Economics, Education, Health, Prediction, Professions, Regulation, Risk

We shall not cease from exploration
And the end of all our exploring
Will be to arrive where we started
And know the place for the first time.

T.S. Eliot – Four Quartets

I feel like I am having a bit of a T.S. Eliot week. I have been writing about the OBR since the beginning of this blog in 2013, with their repeated failures as a forecaster forming the banner for the blog for many years.

What has brought me back to the OBR, as if for the first time, is recalling during the recent Funding the Future conference the words of the very good and wise friend of mine who was there with me. I had invited him to talk to one of my undergraduate groups at the University of Leicester during a professional skills session a few years ago. We were looking at past disciplinary cases of the Institute and Faculty of Actuaries and discussing what the actuaries involved could have done better.

In one of the case studies we considered, an actuary had produced a quarterly reserving report for a company, estimating the reserves on a ‘high’ basis of £74.5 million, a ‘low’ basis of £36.9 million and a ‘best’ basis of £52.1 million. The company then produced financial statements for the year, approved by the board of which he was a part, containing a reserve figure of £15.5 million. My friend told the students that the way to challenge in a situation like this is not to focus on the numbers, but instead see that each different number reflects a different story being told. So you need to look at what the story would need to be for the £15.5 million to be an appropriate figure and take issue with the things that don’t make sense within that story.

It was great advice then and still is, and it is definitely the way to look at the OBR I think. We find a story unsatisfactory if the characters are shallow, do not behave in a credible way, and the plot oversimplifies a much richer situation (something which every story must do to some extent to be manageable to the reader) in a deeply unsatisfying way.

Several other commentators have talked about the inability of the OBR to forecast anything (including me) – this is the equivalent of insufficiently fleshed out characters behaving in unbelievable ways. Richard Murphy, for instance, pointed out how ridiculous the proposed character plotline for inflation was at the last budget:

Whereas in fact of course the downward trajectory predicted at the last budget didn’t look remotely likely even then. The Consumer Prices Index (CPI) rose by 3.1% in the 12 months to August 2026, up from 2.9% the previous month.

However it is Dan Davies’ recent couple of Substack posts (one and two) which get to the heart of the problem in my view. Dan Davies’ contention is that it is the plot oversimplification which is the main problem here. As he says:

The system we have set up only seems to be able to communicate two states though – the signal is either “COMPLIANT – WAR CHEST” or “NONCOMPLIANT – BLACK HOLE”. It would be a significant improvement even for the OBR to add some measure of urgency or non-urgency to its press release – a sentence like “this surplus is well within the forecast error, however, so it should not be used to justify a change in policy”.

But, in his view, it is even worse than that:

In general, most things which a government spends money on will tend to have effects on the economy which go well beyond the five year forecast. The OBR uses an information set which contains only a small and quite arbitrary subset of government decisions – it is, literally, not capable of representing the system it is meant to control at an acceptable level of accuracy.

The justification for ignoring the future consequences of current spending seems to be no more sophisticated than what we pejoratively call “Treasury Brain” – the belief that a) spending departments always claim that their budget should be seen as an investment, b) that they systematically overestimate the long term benefits of current costs and c) that the best solution to a) and b) is to completely ignore any future benefits from investment or future costs caused by noninvestment. It’s just not good enough.

It is this dog’s breakfast that apparently tells us whether we are on track to meet the plotline the government has set for itself. Its three fiscal rules:

Rule 1. The current budget should be on course to be in balance or surplus by 2029/30 (‘stability rule’);

Rule 2. Net financial debt should fall as a share of the economy in 2029/30 (‘investment rule’); and

Rule 3. Some types of welfare spending must remain below a pre-specified level (the ‘welfare cap’).

And if that gruesome threesome doesn’t set your heart racing about the country’s future story, you are not alone. Because the people banging on about fiscal space will always push for governments to spend less, regardless of the circumstances. For instance, at the height of the pandemic in December 2020, Moody’s were saying this (with excess deaths already estimated at over 30,000 in the UK by Public Health England at that point):

However, compared to the government’s March budget (that was quickly overtaken by events), there are some initial signs that fiscal policy outside of investment is likely to be less expansive than previously announced. What remains unclear is whether this ambition will be able to withstand the political pressures that seem to be inevitable given the government’s previous commitments. Even before the Spending Review, longer-term spending commitments for health, education, and defence had already been announced. Together, these three areas account for around 60% of total expenditure.

One of the themes from the Funding the Future conference was, to quote from Educating Rita:

“There must be better songs to sing than this”.

I agree. One possibility is proposed by the New Economics Foundation, who suggest categorising policies by their level of multiplier effect (ie how much economic activity is increased by an increase in spending) and replacing fiscal rules with a “fiscal referee” who can make a more holistic judgement addressing many of the omissions Dan Davies complains about. This would then lead to a discussion rather than the compliant/non-compliant cliff edge we currently have, and allow the government to make its case. And it would not automatically incentivise cuts like the current system does.

Other possibilities exist. As Dan Davies describes it:

any accounting system is a mental prison of some kind, and one which needs to be periodically escaped from

In my view, a jailbreak is long overdue.

The IFoA’s AI Manifesto – a response

01. October 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Pensions, Professions, Regulation, Risk, Science Fiction

A no entry sign over an image of a reverse-centaur where the robot is in control

Last month the Institute and Faculty of Actuaries (IFoA) launched what it calls its “AI Manifesto“. I thought I would take a look.

The Manifesto is admirably short and to the point. Its pitch can be summarised by this final line of its Introduction and context:

For the benefit of society, we encourage those responsible for developing and deploying AI systems to ensure that there is an actuary in the loop.

Think of it as a massive “Gissa job” plea to those companies currently investing in AI systems. But what sort of jobs are we pleading for?

The idea is that actuaries have been working with models which use machine learning for many years, that we have statistical expertise, are comfortable working with data, used to stress-testing systems and also to looking at the risk appetite of a business as a whole.

I think the first hint that there might be a problem here is when the Manifesto moves onto ethics and the public interest. As it says:

Actuaries help teams balance commercial aims with societal expectations, offering constructive solutions rather than imposing limits.

It is difficult to square this with Anthropic‘s recent IPO filing, where they warn its technology may pose “existential risks to humanity”. I am pretty confident that Society expects not to be put at existential risk. So if Anthropic thinks its technology is that dangerous, there is no balance to be found between commercial aims and societal expectations here. Societal expectations are paramount. And limits will need to be imposed rather than “constructive solutions”.

But, in my view, the main problem here is what “real world” the IFoA feel they are serving. This comes through in the role for “professionalism” they envisage:

Projects with sound validation, honest documentation and clear accountability clear governance more quickly, avoid late-stage rework, and fail in testing rather than in production. Professionalism is what lets an organisation say “yes” to AI with confidence – and protects it from the adverse outcomes that would otherwise force it to say “no”.

This, remember, is in an environment where the latest large language models like Anthropic’s Claude Opus 5.5 or OpenAI’s GPT-6 Astra, are proprietary models where validation, documentation, accountability and governance are all just what they let you see. A framework which allows companies to say yes with misplaced confidence is unlikely to be serving the public interest. And translating a capacity to apply a regulatory model to an insurance company where you have all the data and can make informed judgements about the things that you don’t know (Actuaries are used to working with large, imperfect, transactional datasets subject to audit and regulatory scrutiny. We clarify what data is needed, what quality level is acceptable, and how data should be checked, reconciled and monitored over time) is emphatically not the same as working with a model where you don’t know precisely how it works, cannot accurately predict the errors it will make and do not have enough time to look in all of the places you need to.

Take bias for instance. The Manifesto says:

We examine how a model behaves for different groups and sub-groups, test whether training data is representative of the people the system will affect, and help manage the biases we find responsibly through fairness metrics for predictive models, and output audits and red-teaming for generative systems.

Nita Farahany in the law and policy course documented in her Thinking Freely Substack demonstrates the limits of that bias management in practice, and this is Cory Doctorow on red-teaming (from Red Team Blues):

That’s the problem with blue teaming it—you need to be perfect, while—The red team only has to find a single error

Yes, that’s right, the IFoA haven’t even understood that they are on the Blue Team when it comes to AI errors, ie the one supposed to be stopping as many of them as possible. The Red Team breaks into systems by exploiting a single vulnerability. That is comparatively easy. The Blue Team needs to reassure its clients that it has removed ALL vulnerabilities.

That will be the role of any “actuary in the loop”. And the chances are it won’t be possible. The three examples given by the IFoA are as follows:

A generative AI customer assistant
An insurer wants an LLM-based assistant to answer customer queries. An actuary designs the evaluation
framework: a curated test set of realistic queries, explicit tolerances for error, escalation rules for high-stakes topics, and live monitoring of answer quality. The project clears internal governance at the first attempt because approvers can see exactly what “good enough” means and how it is evidenced.

A machine-learning decision model
A lender deploys an ML model to score applications. An actuary tests behaviour across customer sub-groups,
identifies a data artefact that disadvantages one segment, and works with the data science team to correct it. The documentation of that judgement becomes the centrepiece of a constructive conversation with the regulator.

AI risk across an organisation
A Chief Risk Officer faces AI adoption in a dozen business units at once. An actuary helps articulate an enterprise AI risk appetite, maps where the organisation depends on the same foundation models, and builds board reporting that distinguishes experimentation from deployment. The organisation accelerates adoption in low-risk areas precisely because it now knows where the high-risk areas are.

None of these examples look unreasonable in isolation, but, to justify the eye-watering expense of this technology, it will need to increase speed and capacity significantly. So, rather than a single generative AI customer assistant, imagine a client relationship management role where a junior actuary or senior student manages 10 times the current expected number of clients at the same time, because the generative AI customer assistant makes that possible?

Will the skills of an actuary versed in the intricacies of Solvency 2 or pensions regulation be so revered by your client that you would be given more than enough time to rubber stamp a system serving 30 or 40 clients? How long do you think you’d need to check the output on all of them properly? I think it unlikely you will be given that long.

This is the risk of becoming a reverse centaur, where you work for the assistant rather than the other way around. And my view is that the actuarial education system is positively encouraging the development of reverse centaurs amongst our current students.

It gets worse. The early evidence suggests that working with AI systems encourages rubber stamping. In one study of AI-generated audits with an auditor in the loop, 150 of the 160 reports were submitted without any changes made by the human. The performance of humans in the loop may also be adversely affected by working with AI systems, leading to “agency decay”:

Agency decay is the gradual erosion of a person’s ability and willingness to observe carefully, think independently, choose deliberately, and act responsibly. It does not arise because AI is inherently harmful. It arises when convenience becomes the default setting for cognition. A tool that first helps us think can begin to think around us, then for us, then without us noticing what has been weakened.

There are lessons to be learned from other professions who have been working for much longer with expert systems. The aviation industry for example. As Craig Bright, Co-Chief Operating Officer at Barclays has posted:

So the test shouldn’t be whether a human appears somewhere on the process map.

It’s whether the system remains understandable, controllable and recoverable when the agent is wrong, uncertain or unavailable.

Safety doesn’t come from leaving a human in the loop.

It comes from designing a system the human can still control.

The IFoA’s position appears contradictory. On the one hand, they clearly don’t believe that AI is as powerful or as dangerous as Anthropic are claiming in order to push up the IPO share price, or I assume they would not be pushing so hard to get an actuary on every team “developing and deploying AI systems”. I think they are right not to believe it, for a number of reasons, but Naomi Alderman’s AI predictions: seven ways you can tell useful thinking from sci-fi fantasies gives probably the most entertaining ones.

On the other hand, if this is just another technology for which the price is going to need to adjust to reality at some point, we need to be very careful as a profession not to be part of the mob urging their clients to adopt it at a faster rate than they would otherwise. Particularly if it is damaging our own professionals in the process.

AI in some form will survive the inevitable crash, but the shape and scope of the wreckage that will be left behind is currently hard to predict. Let’s be careful what we wish for.

Normals: new and otherwise

25. August 2026 · Write a comment · Categories: Actuarial, Climate Change, COVID-19, Economics, Health, Media & Politics, Risk

Climate campaigners get very irritated with media talking about the “new normal”, pointing out, quite reasonably, that for it to be the new normal would require a degree of climate stability that we have not got, nor have any prospect of getting while we continue to argue about whether net zero (the policy, remember, which is about moving us to a position where we don’t increase the amount of carbon in the atmosphere year after year) is affordable. Poverty and health campaigners got very irritated by the call to “get back to normal” after the pandemic, when it had so clearly demonstrated how our “normal” way of organising society had failed to protect so many of its citizens. As even I could see in March 2020:

My view is that some things that must be different post COVID are already clear. I think as a society we are going to demand more resilience, for example:

Resilience of our health service – this means much higher levels of spending, building deliberate over-capacity into the system in normal times;
Resilience of our food supplies, for example strengthening domestic supply chains;
Resilience of our population, so that we do not have 1.6 million food parcels needing to be given out in a year by the Trussell Trust, in the absence of a pandemic, for instance; and
Resilience of our infrastructure – to everything from floods to banking crises to pandemics to storms and heatwaves.

A picture circulating at the time of a Santiago apartment block with a projection onto it of “No volveremos a la normalidad porque la normalidad era el problema” (we won’t get back to normal because normal was the problem) was actually taken the previous year when the Chilean President had used the word “normality” to justifying lifting the state of emergency, leading to large protests:

Source: https://x.com/inesmorsantos/status/1190192093165211648?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1190192093165211648%7Ctwgr%5E%7Ctwcon%5Es1_&ref_url=https%3A%2F%2Fodi.org%2Fen%2Finsights%2Fcovid-19-we-wont-get-back-to-normal-because-normal-was-the-problem%2F “We won’t go back to normal, because ‘the normal’ was the problem.”

But we all have trouble with fast moving situations, or even just moving situations. Remember the media coverage in the early weeks of Covid utterly focused on the number of cases going up each week rather than on the underlying pattern this showed, which had been predicted and could be responded to.

In the Institute and Faculty of Actuaries syllabus, subject CS2 Risk Modelling and Survival Analysis has 20% of its content on dealing with our trouble with moving situations via time series:

It boils down to a number of tricks to turn our moving situation into a non-moving or stationary situation, which we can then analyse.

So not being able to deal with things that aren’t stationary is entirely normal. Indeed the Standard Normal Distribution, originally developed in response to a gambler’s query and another thing that we spend a lot of time transforming other distributions into if we can, has a fixed mean of 0 and a fixed variance (a measure of how far things stray from the mean) of 1:

Outside statistical analysis, we use different techniques to make things appear normal. Denial, ie ignoring the departure from normality, is a big one. When this one is no longer tenable in the face of mounting evidence that we are not in Kansas anymore, we often try and depict where we have got to as normal now. This will have been a good strategy in the past: once a natural disaster has passed, taking stock of what has been lost and what remains and working within these new parameters makes more sense than hankering back to how things were before the disaster struck.

However this strategy comes unstuck when one or both of two things are true:

The disaster, or change-event more generally, is not a one-off but a continuing process.
The way we were living before the disaster made the disaster more likely.

So, on climate change, both 1 and 2 are true. On the pandemic, 2 (if you define the disaster as the number of people who died during it, which I do) is clearly also true. And, although it may not be a continuing process, none other than the former Chief Scientific Advisor to the UK Government regards another pandemic as “inevitable”.

We have built systems to look at past disasters and, in particular, try and guard against 2. The Covid Inquiry is working its way through the evidence about the pandemic, and has already produced definitive evidence on how we could do things differently for those who wish to read it, but the reporting of its work is patchy, with sometimes unhelpful fixations on the wrong thing.

And denial tends to fight back as our go-to defence mechanism, which is why we often seem to need to have Public Inquiries about the same things again and again and again.

It is all quite normal. That is the problem.

Nutted by Newton

03. July 2026 · Write a comment · Categories: Actuarial, AI, Economics, Education, Professions, Risk

Isaac Newton may or may not have been nutted by an apple. His friend William Stukeley, whose memoir of Newton was the source of the story, states it as follows (spelling and punctuation from Stukeley’s manuscript at the Royal Society):

“why should that apple always descend perpendicularly to the ground,” thought he to him self: occasion’d by the fall of an apple, as he sat in a comtemplative mood: “why should it not go sideways, or upwards? but constantly to the earths centre? assuredly, the reason is, that the earth draws it. there must be a drawing power in matter. & the sum of the drawing power in the matter of the earth must be in the earths center, not in any side of the earth. therefore dos this apple fall perpendicularly, or toward the center. if matter thus draws matter; it must be in proportion of its quantity. therefore the apple draws the earth, as well as the earth draws the apple.”

However there may be stronger grounds for believing that Newton nutted English mathematics and, as a result, the actuarial profession.

I thought all this to myself, occasion’d by participation in the Actuarial Teachers and Researchers Conference (ATRC) this week. It was great. There were lots of interesting talks, and people were very engaged and respectful in how they discussed them. There was a lot of expertise in the room about both education and actuarial practice and theory. It felt like it was a room that could take on difficult topics and make progress in tackling them.

There were several calls for the need to change assessment, one of them by me, whether due to large student cohorts and the difficulties of engaging them, or the ease with which AI can duplicate examination solutions or because the credentials provided by those assessments seem increasingly irrelevant to the actual actionable skills, knowledge and experience required to operate successfully as an actuary. And there was an equally robust response from the Institute and Faculty of Actuaries (IFoA) to the effect that assessments would not be changing significantly in the latest changes due to come in by 2029.

My favourite presentation was by Angus Macdonald, about his recent short paper on Newton, Leibniz and Actuarial Science. It conjectures that the argument between Leibniz and Newton over who invented calculus resulted in the stagnation of mathematics in English universities and meant that actuaries needing to gain respectability for their advice were forced to create a professional body (in company with architects, accountants and engineers) rather than rely on universities to develop actuarial thinking as happened in continental Europe. This stagnation (Macdonald quoted G.H.Hardy, talking about the Cambridge mathematics exams, saying that they had “. . . effectively ruined serious mathematics in England for a hundred years”) was felt across the whole Anglosphere, with attempts to create institutions with equal status to the traditional universities leading to the establishment of business schools in the USA in the late 19th and early 20th centuries.

This explained a lot for me. The suspicion of our actuarial courses amongst some of our mathematics colleagues at times at Leicester. The occasionally uneasy relationship between the IFoA and the accredited universities where students can gain exemptions from their exams. The horror from some at the very idea that an actuary might qualify as a result of university courses, like they can in the Netherlands, for instance. ATRC seemed the appropriate place to explore these ideas.

Angus Macdonald considers that the Newtonian and Leibnizian branches have nearly rejoined, but I feel that our actuarial education system still suffers from the long shadow cast by these two 17th century gentlemen and their personal enmity. The ferocity with which some within the profession opposed what they saw as a threat to the popularity of the fellowship qualification posed by the new chartered actuary designation was hard to explain when the number of practising certificates issued for chief actuaries, pension scheme actuaries, etc was only around 1,100 (out of over 17,000 fellows), but if the whole profession originated from a highly developed form of status anxiety compared to academics, it becomes much easier to understand. As they say, the apple doesn’t fall far from the tree.

Now, nearly 180 years on from this “unstoppable” “rush to respectabilize” (according to Jeremy Paxman) I think it is legitimate to ask:

Why does the IFoA still insist on detailed accreditation of individual university courses on basic mathematics, statistics and business economics? Do they not trust them to teach them right? Do they think that there is something better about learning these universal subjects via a bespoke course created by a tiny professional body with 34,000 members globally?
Why are they so concerned about what assessment methods the universities use? Do they consider that they have more educational expertise on this than some of the leading universities in the world?

I have written previously about the changes in assessment I think are necessary and floated some ideas about where actuarial education might sit to accommodate these. My view was that the IFoA, faced with the need to innovate at all levels of its education system at a time of great uncertainty, might wish to get out of the foundational mathematics and business education tuition of the core principles subjects and leave this to the university system, in a truly Leibnizian way.

However there is a problem. The university system is facing a tough time. UK higher education is shrinking according to the UCU branch at Queen Mary’s, which has set up a a live page of all the redundancies, restructures, reorganisations, and closures taking place across the UK Higher Education (UKHE) sector. The numbers from the Higher Education Statistics Agency (HESA) bear this out, with overall student numbers falling, driven by a 10% fall in overseas entrants with a non-European Union permanent address.

In actuarial science, the list of UK universities offering accredited courses has not changed for some time, and it is a small group within the university system. These courses must be seen as vulnerable. I don’t think it is a coincidence that it is so hard to persuade one of this tiny band to run the ATRC. This week’s was the first since 2021 and the list of past hosts is not very long:

The IFoA needs to ask itself what it would do if there were some high profile closures of actuarial courses and whether perhaps it might be time to protect the core mathematical education arrangements for actuaries through a broad brush accreditation of a wider range of courses, rather than the very detailed accreditation process it currently carries out on core principles subjects.

I also doubt that it has the capacity on its own to significantly restructure its assessments beyond the objective based assessments it has developed to work within its proctored environment.

The core practice and specialist subjects leading to fellowship are the areas where actuaries have, rightly, the strongest views about content and assessment structures. It surely makes sense that the expertise within the profession and the university system are pooled in a joint endeavour rather than risking both failing independently.

In contrast to Newton, who had quite a temper, Leibniz was famously parodied by Voltaire in his Dr Pangloss character in Candide who believed that “All is for the best in this best of all possible worlds.”

Let’s not be too Panglossian about the current state of actuarial education.

Reservoirs: this raises the question of what is meant by “safe”

24. June 2026 · Write a comment · Categories: Birmingham, Climate Change, Media & Politics, Regulation, Risk

This week I attended one of the National Emergency Briefing screenings near me. While I wasn’t a fan of the Celebrity Gogglebox presentation style, with Chris Packham on the sofa seeing what Jennifer Saunders and others thought about the climate emergency, it did give me enough of an idea about the individual presentations to want to follow them up, and there is a really good website to do so here.

The two points which struck me particularly were the following:

Professor Tim Lenton warning about what would happen if the weakening Atlantic Meridional Overturning Circulation (AMOC) hit a tipping point which, in some models, “shows that London would be -20°C in three frozen months of the year, and Edinburgh would be -30°C in five and a half frozen months of the year. And yet the summers will still be hotter than today because it’s a 2°C warmer world.”
Professor Hayley Fowler warning of the risks of mega floods: “These storms can produce two thirds of a year’s rainfall in just a couple of days. Over London, that would mean about 35cm of rainfall falling over a large area. A flood of this scale will be a national crisis. Recovery would take years.” She also warned that by 2050 1 in 4 properties – 8 million in England – will be at risk of flooding.

And since flooding often follows a heatwave such as the one we are currently experiencing, I thought I would look at the risk of long-term flooding in a bit more detail, which is when I came across the government’s check the long term flood risk for an area in England website. When you put in your postcode, it assesses your flood risk under four different categories:

It then explains how they measure the risk and the kinds of things which tend to affect it. All in all a good resource, and the modelling behind it is what the 1 in 4 properties at risk of flooding by 2050 is based on.

In July 2021, several European countries were affected by severe floods. The floods started in the United Kingdom as flash floods causing some property damage. Later floods affected several river basins across Europe including Austria, Belgium, Croatia, Germany, Italy, Luxembourg, the Netherlands, Romania and Switzerland. At least 243 people died in the floods, including 196 in Germany, 39 in Belgium, two in Romania, one in Italy and one in Austria. It was thought that some of the affected regions may not have seen rainfall of this magnitude in the last 1,000 years.

A study using climate simulations on a grid of 2.2km squares published in June 2021 had come to the following conclusion:

Intense rainstorms are expected to be more frequent due to global warming, because warmer air can hold more moisture. Here, using very detailed climate simulations (with a 2.2 km grid), we show that the storms producing intense rain across Europe might move slower with climate change, increasing the duration of local exposure to these extremes. Our results suggest such slow-moving storms may be 14× more frequent across land by the end of the century. Currently, almost-stationary intense rainstorms are uncommon in Europe and happen rarely over parts of the Mediterranean Sea, but in future are expected to occur across the continent, including in the north. The main reason seems to be a reduced temperature difference between the poles and tropics, which weakens upper-level winds in the autumn, when these short-duration rainfall extremes most occur. This slower storm movement acts to increase rainfall amounts accumulated locally, enhancing the risk of flash floods across Europe beyond what was previously expected.

As Professor Fowler says:

Our infrastructure was built for a climate that no longer exists. Raised reservoirs, drainage, housing and transport were designed many years ago when extreme rainfall was rare and less severe. As rainfall intensifies, risks such as dam overtopping and cascading failures rise.

The government also produces monthly reports on rainfall, soil moisture deficit, river flows, groundwater levels and reservoir levels. This includes a wonderful map of the year’s rainfall:

What struck me was that the report looked at exactly the same things (river levels, groundwater levels and reservoirs) but, because they were concerned with low levels of rainfall, did not even mention flood risk. The rainfall scale only goes up to >125mm rainfall within a month as a maximum, which looks hilarious when compared with the 271.5mm of rainfall over 48 hours in parts of Belgium in 2021.

There have been no new reservoirs built in the UK since 1992, just another one of the gifts of water privatisation discussed previously. The government announced last year that it had “seized control of the planning process” to build two reservoirs due to be complete in 2036 in the Cambridgeshire Fens and 2040 near Sleaford in Lincolnshire respectively.

Professor Fowler says the probability of dam overtopping is increasing. So how’s the review of reservoir safety going?

In 2019, there was a failure of a dam at Toddbrook Reservoir in the Peak District. The operation to pump water out of the reservoir to safeguard Whaley Bridge seems to have been dominated by concerns about the fish. The independent review commissioned into the incident identified the rather alarming fact that:

“…the reservoir and its Owner can be compliant with the legislation without the reservoir necessarily being safe.”

Among the report’s recommendations were the following:

There is a systematic review of how the current Reservoirs Act, and the associated Regulations and Guidance, are implemented. This should consider the roles and responsibilities of qualified engineers, whether compliance with the Act is sufficient to ensure safety, and how safety is formally assured.
The potential of an Inspecting Engineer issuing a Certificate of Safe to Operate be explored. This should include a review of practice in other safety critical infrastructure sectors. It should also consider liability implications and whether some form of qualifying statement may be needed to accompany the certificate.
The statutory maximum period between inspections is reviewed to determine if it is still appropriate in every case in the light of the ageing reservoir stock.

In his follow up report looking at the reservoir system in general in 2021, Professor David Balmforth made this extraordinary statement:

This raises the question of what is meant by “safe”. In other infrastructure sectors and with reservoirs in other countries, safety is assured by managing risk, and by reducing that risk so far as is reasonably practicable – the terms “reasonable” and “practicable” being well understood in practice and in law. For a reservoir, risk is defined as a combination of the likely failure of the dam (or other reservoir structure) and the impact that an uncontrolled release of water would have on the area downstream, particularly the likely loss of life. I have therefore recommended that in future the assurance of reservoir safety should be managed on the basis of risk, and that the amount of effort (and cost) associated with that process should be in proportion to that risk. In this way the public can be assured that the hazard posed by reservoirs is being managed in an objective and transparent way.

It would appear that reservoir management is playing catch up on the whole concept of risk management.

What has followed is a policy paper from the Environment Agency on the launch of a reservoir safety reform programme. However the public consultation on the proposed reforms has already been delayed until later this year. And the page about the consultation process does not suggest anyone is in any hurry.

How Not To Be A Reverse-Centaur – Part 2

16. June 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Economics, Education, Professions, Risk, Science Fiction

As I discussed in How Not To Be A Reverse-Centaur (Cory Doctorow defines a reverse-centaur as “a machine that is assisted by a human being, who is expected to work at the machine’s pace”) our actuarial education system needs to change. This has been true for some time, but the development of AI systems has both demonstrated why more clearly and accelerated the timeframes over which action is needed. As I said in December last year:

A large part of the education of the future will need to be about equipping us all to understand what we now have access to and when and how to access it. We will all have different things we are interested in, or end up involved with and needing to be educated about. It will be up to each of us to decide which things are worth the difficulty of learning, but to make those decisions we will need education that can support the development of judgement.

For education institutions, the question will be what is not worth the difficulty of learning? Credentialising based on now relatively meaningless assessment methods will not cut it. This is where the confrontation with employers and politicians is likely to come. Essential skills and their related knowledge will be better developed and assessed via more open-ended project work and online assessment of it to check understanding. These will need to become the norm, with written examinations becoming less and less prevalent. Not because of fear of cheating and plagiarism, but because an outcome which can be replicated that easily by AI is not worth assessing in the first place.

Suppose you were to ask someone who had never seen our academic system that, in order to assess whether someone else we wanted them to employ in their organisation would do a good job for them, we would:

Award marks for what has been written in answer to academic questions about what the students can remember unaided about the content of their lecture courses and reading lists with a biro on a pad of paper perched precariously on a tiny wooden table surrounded by hundreds of other similar scribblers, for a set period of time as minders wandered the floors like Victorian factory owners.
These marks would be based on marking criteria they would never see.
We would then get together in a secret huddle for a couple of months to mark all these scripts, check them and “moderate” them to check they were in line with the previous years’ exercises.
Then out would come the answer (like the Answer of 42 to the Ultimate Question of Life, the Universe and Everything in Douglas Adams’ The Hitchhikers Guide To The Galaxy).

Source: https://hitchhikers.fandom.com/wiki/42

Imagine another world where the kinds of things employers routinely do when trying to decide whether to take on someone as a new member of their organisation are routinely part of their assessments at university, eg:

Watch them operating in groups working on a task.
Ask them to describe how they would go about tackling a particular problem.
Get them to show they understand the implications of a piece of work they have carried out and can explain what assumptions it is based on and can justify them.

With assessments of performance which are not opaque academic exercises but descriptions of performance made as the tasks are being carried out evidenced by video if necessary. In other words, transparent enough to whoever might want to work with these students next to eliminate the gap between their credentials and and their actual actionable skills, knowledge and experience.

Assessing in this way will be highly demanding, for both the students and the assessors, but it means the final assessment is done with the student present and, with careful probing from the assessors, who will obviously need to have done a close reading of the project work beforehand, confidence will be high about the abilities demonstrated by the process. And it removes the need for all the time currently spent on the Victorian factory owner process – imagine what could be done if the whole of May, June and July wasn’t spent marking scripts!

A move like this will significantly change what is taught. There will be no point presenting lots of information in ways which make retention easier for students (the so-called bookwork” questions currently in most exams). This makes sense when you think about how much of the content of your courses is accessible to you now, even on a vocational course like actuarial science, as opposed to skills developed. Instead the focus will be more on conversations with students for the purpose of developing their understanding of a meaty topic and the problems to be solved within it. Explorations of these will form the projects students will be spending most of their time working on. Checking understanding and making sure students are working within frameworks they understand and can talk about will be the main teaching activity, with opportunities to practise these throughout the year. In other words, showing the ability to apply what they have understood and demonstrate the ability to reflect, make judgements and show higher levels of understanding as a result. An ability they can then apply to new problems and situations.

And notice something else about how these alternative credentials are generated? They are all social activities: from operating in groups, to presenting a piece of work to a group of assessors. Even the project work is developed through group sessions alongside individual work. If you want to develop people who can spot the gaps between models and reality you need to do it in groups, where people can test their perspectives against each other.

Compare this to the current solitary exam preparation, peppered with “revision” lectures, exam sitting and exam marking processes which comprise the predominant activities in universities between March and July each year. We are currently being funneled into an ever more solitary professional practice around LLMs. It has become quite accepted for people to do all of their thinking on a subject closeted with perhaps one, two or three LLMs but no other people and then share their analysis with an expectation that other people should read the output. Developing the skills to do these analyses will obviously be necessary, but they will never be the most important skills people learn.

Treating each other like walking databases is never going to make you new friends or influence people. However the skills you develop via an alternative more social education will.

There are examples of this approach already happening, with employers involved in the design of assessments in some cases and universities using the flexibility they have to restructure and redesign courses. But the dominant assessment system of formal exams is keeping much of this activity at the margins at the moment.

If it were to become the dominant assessment, it would also mean that larger areas of the curriculum could be examined, as the focus would not be on retention of large bodies of content but the ability to use the content to solve problems. So, perhaps, after a first year levelling up students’ ability and experience of mathematics and statistics, year 2 would have an actuarial statistics module (currently CS1/CS2) and an actuarial mathematics module (currently CM1/CM2), both dissertation based and vivaed. Then in the third year they would tackle an economics module and a business modules (currently CB1 and CB3). Perhaps in the final year of a four year MAct they would tackle a modelling and communication module (CP2/CP3) and either CP1 actuarial practice module would be offered or a professional skills module.

This would be more of a driving test style of assessment, with students working with supervisors on the comments from assessors on the original dissertations and presentations until they were up to the required standard. Students unable to make the standard after an additional year would be offered a move to a non-accredited course. The current funding model for higher education will need to change: loading students with £10,000 more debt every time they add to the nation’s skills bank with another year wrestling with these difficult skills to master was never a clever strategy, but this structure would make it even clearer how self defeating it is. If economists are accepting that the labour market no longer allocates resources effectively and are considering either a universal basic income or universal basic services, then logically university funding should follow suit.

The implications for the actuarial profession will be even more challenging. I question whether the Institute and Faculty of Actuaries will continue to consider it worth the effort and expense of reconfiguring their entire approach to exam setting, syllabus maintenance, marking, online proctoring, etc for subjects routinely taught throughout the university system for a a variety of purposes, ie the current core principles subjects of mathematics, statistics and business studies.

Whether they retain their own capacity to assess the core practice subjects is a more open question (CP1 Actuarial Practice, I imagine, will be seen as sufficiently specialist to keep in house, with perhaps a few universities, as now, accredited to run courses which can earn this qualification, however CP2 Modelling and CP3 Communications are not nearly as specialised now as they seemed when they were first introduced). I would also move the new core economics module recommended by the IFoA Economics Review Group I chaired in 2022 to replace the current CB2 module into the core practice section, as it would be more of a critical thinking module about the economic ideas necessary to underpin good actuarial work than the technical how-to subjects in core principles.

The specialist stage of the SP and SA subjects and the whole continuing education and additional certification options I would confidently expect the profession to retain, review and adapt as needed to reflect new challenges in members’ working lives. However, if CP1 is all that is needed post graduation to get to Chartered Actuary and CP1, two SP subjects and a SA subject gets you to Fellow, then suddenly time to qualification becomes much more predictable for students and their employers alike.

The risks to employers of taking on students would therefore be reduced, but also the rewards to taking them on will be much more transparent. The students we will be developing will have collapsed the gap between their credentials and their actual actionable skills, knowledge and experience. They will have:

Great team working skills;
Very highly developed presentation skills, both in writing and speech;
Strong IT skills and comfort working with data; and
Clarity about why they are in an organisation and a drive to use their skills to solve problems.

They will also have developed the four capabilities set out in Carlo Iacono’s Teach Judgement, Not Prompts:

Epistemic rigour. They will be more likely to spot when a system or model is over-confident given the evidence, after an intense experience of interrogating models and evaluating evidence in their courses.
Synthesis. They will be able to integrate different perspectives into an overall understanding, through dissertation development, defending a position, understanding weaknesses in a position and adjusting accordingly.
Judgement. They will have been asked to make many judgements in their work and had to defend them in discussions with their supervisors and fellow students. Unconvincing opinions, not backed by evidence, will not pass muster.
Cognitive sovereignty. Students will have to take their own stand on their work, after all of the challenge and argument. Independence of thought is the hard-earned outcome here.

And then they will have a fighting chance of being centaurs in the world awaiting them, masters of the technology and opportunities available, rather than the reverse-centaurs that our education system is currently, and inadvertently, preparing them to be.

The meatspace layer for actuaries

20. May 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Economics, Education, Media & Politics, Pensions, Professions, Regulation, Risk

A suited pinhead wearing a pirate's hat stands in the stern of a pirate ship below a dangling ladder — Source: Nick Foster – December 2013 – originally drawn to deride George Osborne’s austerity as Pugwash Economics, repurposed now as I am worried about the actuarial profession pulling up the ladder on the next generation

The “black box” was a constant refrain when I was working as an actuarial consultant. It was where the results from a process were being accepted without any understanding of how they were arrived at. Something we felt that any self-respecting actuarial consultant should challenge in their own work and everybody else’s.

However when you came to actually present analysis or arguments to a client, you expected a certain amount of that expertise to be taken as read, to effectively be inside a black box as far as the client was concerned. They couldn’t be expected to understand all of the aspects of what you were talking about, otherwise they wouldn’t need you. Good practice was always to put them in a position where they could understand and make decisions about the key aspects of your advice without needing to engage with the other parts. As the expert, you decided what was in the black box.

Now the black box is back with a vengeance for all the professionals who have relied upon them in their working lives. As Dan Davies puts it:

The same black-box property which stops you from being second guessed or overruled means that nobody is interested in your explanations for your decisions; it is definitional of being a black box that you are going to be judged by results.

And, if you are in the business of advising in the teeth of uncertainty, as actuaries are, then this is likely to be a real problem. If noone cares how your advice was constructed, but they can get advice that ticks the compliance box your client has to complete more cheaply and quickly than you can, the more automated black box is going to win the business. The experienced professional still has a role in managing this process, verifying the results coming out of the black box and determining what can still be kept out of the black box, but he may be increasingly struggling to justify the cost of his junior colleague.

I wrote about how devastating the fall in graduate job listings was 9 months ago, so where have we got to since?

Well things don’t look so bad in the UK right now according to the Office for National Statistics (ONS), reverting to close to the average after a post pandemic surge in the finance and insurance sector:

*Source: https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/datasets/vacanciesbyindustryvacs02*

However, if we look at the United States, which tends to show us where the UK finance sector is going, it looks far more ominous:

Yesterday Sky News ran a story about Standard Chartered‘s CEO who, in his desperation not to describe over 7,500 job losses as cost cutting, said this:

It’s not cost-cutting. It’s replacing in some cases lower-value human capital with the financial capital and the investment capital we’re putting in.

We may need to sit with that statement for a little while.

Daniel Susskind talks about this risk in his latest lecture entitled A World Without Work: in summary, to paraphrase only slightly, sure relatively junior white collar roles may already be particularly hard hit by AI, but he is optimistic because of the impact on GDP and we cannot pause because of China. He then goes on to talk about the four problems he sees for a post AI future:

Distribution (replacing wages);
Contribution (how do you “pull your weight”);
Power (domination by Big Tech on economics, politics, liberty, social justice and democracy); and
Meaning (fulfilment in life).

Susskind has gone from thinking that the fear that AI is coming for your jobs was overblown and that it was just task encroachment that we faced, to now thinking that it may encroach on all the tasks in most fields. Jevons Paradox (that technological innovation that increases the efficiency of a resource’s use leads to a rise in consumption of that resource) is no comfort if that new demand is robot-met.

Carlo Iacono suggests that the move of junior roles to AI may be subtle to begin with:

The weakness among young workers may appear as fewer people entering employment from outside the workforce. Firms may not fire large numbers of juniors; they may simply hire fewer of them.

That matters. The labour market can look healthy while the entry path narrows. Senior workers stay employed. Output rises. Productivity improves. There is no dramatic wave of redundancies.

Yet the first rung is being taken out.

It may also be masked by the fact that there remains a shortage for actuaries beyond the entry roles. There is almost a hint of desperation to approaches like this looking for introductions from a retired actuary like myself:

(followed by a list of clients he is working for)

Source: recruitment consultant who will remain anonymous. I am assuming “candies” are candidates

And, even if you think the risk of the AI Bubble bursting soon, taking down the global stock markets underpinned by the Magnificent 7, is exaggerated, you do need to be suspicious about the current abilities of AI to replace junior staff. My experience with another, somewhat earlier, actuarial technology, the pensions valuation engine, would suggest that the outputs need to be analysed very carefully before sharing with a client: it often had dependencies between what should have been independent variables hidden in the programming, or vagaries in the setup which left out non-standard benefit rules for your particular scheme, for instance. Or the student who had set it up initially (a complicated process usually) might have made a mistake or you might have not communicated with them very well to start with. Or a hundred other things.

For whatever reason, there was often still a lot to do after the valuation engine had produced some output.

Can this sort of thing happen with agentic AI? Well think about that student programming the valuation engine, but on steroids. Its patchy capabilities combined with its basic psychopathy leads to, as Hannah Fry entertainingly demonstrates here, some serious problems arising with the agent’s relentless to and fro with the large language models it depends upon, asking them what it should do next. As Hannah says:

I built an AI agent. She opened a shop selling novelty mugs, emailed a journalist without being asked, and then leaked our passwords to a total stranger.

As Kyle Kingsbury wrote about having an AI agent as a colleague in a programming team:

Imagine a co-worker who generated reams of code with security hazards, forcing you to review every line with a fine-toothed comb. One who enthusiastically agreed with your suggestions, then did the exact opposite. A colleague who sabotaged your work, deleted your home directory, and then issued a detailed, polite apology for it. One who promised over and over again that they had delivered key objectives when they had, in fact, done nothing useful. An intern who cheerfully agreed to run the tests before committing, then kept committing failing garbage anyway. A senior engineer who quietly deleted the test suite, then happily reported that all tests passed.

You would fire these people, right?

Yet despite all this, the money continues to pour in to the construction of AI infrastructure. There are already websites up and running for all of the parts of tasks AI cannot encroach upon.

The bottom rung of the actuarial ladder is clearly in danger. This is a particular problem for the actuarial profession, which has traditionally relied on longer periods of work-based training for its future qualified actuaries than many other professions. Training to become an actuary takes a long time. Median time to fellowship is still around six years, with some taking up to ten or giving up. The exams are hard to pass. There have been attempts by the profession to tackle some of these disincentives: the Chartered Actuary designation to make a destination of the generalist qualification before the specialisation of the fellowship, championed on this blog and launched in the teeth of opposition by some fellows, being one example.

It has led to a culture within actuarial firms around managing the extended time in training, with rituals around study leave and results days. One of the fears expressed in opposition to the introduction of the Chartered Actuary designation was that, if this could be achieved almost entirely within formal education at universities, the value of working alongside experienced actuaries would be lost.

It has led to a culture within the profession itself of managing large parts of its education system in house. Half of its revenue and around 30% of its expenditure are on “pre-qualification learning and development”. Sometimes it looks more like an education business with a professional side hustle.

But then the new AI toys have come along, and it turns out that many of those experienced actuaries may be less keen on graduates coming in and needing supervision from them after all. Many of them may rather spend hours on AI prompts than on developing another human being.

I fear that, increasingly, companies are not going to accommodate actuarial students in their work plans without significant persuasion. And, if the number of students studying while in work falls, the profession itself is going to struggle to finance its own bespoke education system at an acceptable cost to its members.

It will be hard for the profession to challenge this too: it is going to be good for many of those already established in their roles as the market for more experienced actuaries, when the market has no interest in developing the actuaries of the future, becomes increasingly competitive.

If the actuarial profession does accept the challenge of protecting the pipeline of future experienced actuaries it will need to review its entire education syllabus through this lens. It will also need to engage with other partners involved in what is in effect a problem of capital formation and collective action: government incentives may be needed to encourage firms to continue to train early career professionals and discourage free-riding. There may be no way back for the student with no actuarial qualifications learning on the job. The universities may be needed to plug people in at a different career point, which will require them to innovate themselves even further into the professional training role than ever before. As Carlo Iacono points out:

educational institutions may be pushed to simulate more of the apprenticeship environment. That does not mean adding a thin “AI literacy” module. It means creating settings where students practise judgement under uncertainty, in realistic workflows, with feedback that is close enough to hurt and useful enough to teach.

It will not be at all easy. But the alternative is a future without opportunity for those who do not already have it and an ageing profession withering on the vine it refused to nurture.

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