The articles in this blog series will follow the 7 lifecycle stages set out in this introductory piece. Each one will focus on what is actually being done at the current stage, what it takes to do it responsibly, and what it means for how investment actuaries work. We will keep them short, practical and – where the evidence warrants it – genuinely enthusiastic about what is possible. Watch this space. There is a lot to cover, and we are only getting started.
“I can calibrate your capital market assumptions, stress-test your SAA against five hundred distinct macroeconomic scenarios, flag every inconsistency in your investment committee pack, and produce a first draft of your CIO's quarterly market commentary — before your 9 am. What I cannot do is exercise the investment judgement, accept the regulatory accountability, or take professional responsibility for the advice that sits behind those outputs. That part still needs you. But — and this is the part worth sitting with — a portfolio manager, an insurance investment strategist, or an investment consultant who uses me will do ALL of that with greater depth, greater speed, and a broader evidence base than one who doesn't. The investment actuary who engages is not being replaced. They are being upgraded. The one who doesn't? They are not being prudent. They are just making themselves optional.”
– Claude (Anthropic), asked to be candid about the future of the investment actuary
Right. We will take that as our cue.
This series is not here to make you feel guilty about the spreadsheet you still love, or suggest that all these years of actuarial training are suddenly worthless, or imply that the entire profession needs to immediately drop everything and learn Python.
We are also not here to host another round of the ‘great AI ethics debate’ – that conversation matters and we’re glad it’s being given due attention, but this is not the working party for that. Think of it less as ‘actuarial god versus the machines’ and more as ‘AI-enabled actuary versus everyone still doing it the old way.’ Pick a side. One of them gets to leave the office (virtual or otherwise) on time.
What we are here to do is considerably more useful and hopefully less stressful: show you, stage by stage, where AI is already reshaping the investment actuarial work you do every day – and where the biggest opportunities to work smarter, faster and more impactfully still lie ahead.
We are a working party of actuaries from across investment management, insurance, pensions and consulting. We came together because we kept having versions of the same conversation – tools we were experimenting with, hours being saved on tasks we used to dread, analyses that had become richer without becoming slower – and felt the profession deserved a grounded, practical account of what is actually happening at the coalface of investment actuarial work. Not hype. Not alarm. Just: here is what exists, here is how it works, here is what it means for the job.
We will also, towards the end of this article, ask something of you directly: we are running a survey on AI use in investment actuarial practice, and we would welcome your input.
The lifecycle runs from the big-picture macro assumptions that frame every investment decision, all the way through to the reports and disclosures that close the governance loop. 7 stages, one continuous arc. Investment actuaries across asset owners, insurers, asset managers and consultants touch all of them.
1. Capital market assumption (CMA) setting is where it all begins – forming long-run views on returns, inflation, yield curves and credit spreads. These are the inputs that drive everything downstream: SAA, ALM, scenario generation, liability discounting. Get them wrong, or get them late, and the error propagates through the entire process. In practice, this is already changing: large language models (LLMs) are synthesising central bank communications, macro data releases and geopolitical commentary into more timely and comprehensive inputs for economic scenario generator (ESG) calibration, compressing what used to be a manual, periodic process into something that can be refreshed continuously.
2. Strategic asset allocation (SAA) translates those views into a target portfolio structure, balancing liability profiles, regulatory capital constraints and risk appetite. For insurers this means optimising against SCR consumption. For asset owners it means aligning the portfolio with funding objectives and journey plans. Here, neural network surrogates or proxy models are being deployed for Solvency II SCR calculation, replacing computationally brutal nested Monte Carlo simulations with faster approximations and enabling iterative optimisation of asset allocation against capital constraints that would previously have been impractical to run at all.
3. Portfolio construction takes the strategic target and turns it into an actual portfolio – deciding which asset classes, in what proportions, with what constraints, and how to respond when markets shift the starting assumptions. Machine learning models for regime detection are now identifying structural shifts in market behaviour earlier and more reliably than conventional methods, helping portfolios adapt rather than remain anchored to assumptions the market has already moved past.
4. Implementation is where the portfolio meets the market: security selection, credit analysis, issuer assessment and the signals – increasingly derived from non-traditional data sources – that distinguish good investments from the rest. Natural language processing (NLP) tools applied to earnings call transcripts and corporate filings are already providing earlier credit signals than rating agency actions typically deliver – directly relevant where deterioration needs to be caught before it becomes a capital event.
5. Risk monitoring tracks whether the portfolio is behaving as intended and whether risks are quietly accumulating in the background. Early warning matters here: by the time risk is visible in performance, the interesting question is usually already too late. AI early warning systems are detecting the precursors of market stress significantly earlier than conventional approaches – the difference, for an insurer’s own risk and solvency assessment (ORSA) stress testing, is between a genuinely dynamic process and an annual point-in-time exercise.
6. Portfolio management and rebalancing takes the output of monitoring and asks: is the drift material, and what should we do about it? In liability-aware portfolios this includes hedging ratios, SCR consumption for insurers and the ongoing eligibility of matching adjustment portfolios. AI-driven rebalancing tools are now flagging breaches of target ranges continuously rather than at the next scheduled review, and pre-screening trades for transaction cost efficiency before they reach a portfolio manager’s desk.
7. Reporting and governance closes the loop – communicating what was done and why to investment committees, boards, regulators and clients, and ensuring the various documents produced along the way are consistent, accurate and defensible. Generative AI is already in production use at a number of firms for first-draft investment commentary, cross-checking report consistency and accelerating disclosure workflows – freeing actuarial time for the review and sign-off that actually requires professional judgement.
Here is the genuinely encouraging part: actuaries are well placed to deploy these tools well, because the skills required to use AI responsibly are skills actuaries already have.
The actuarial control cycle – define the problem, build the model, validate it, monitor it, revisit the assumptions – is a governance framework for working with uncertain models under professional accountability. That is precisely what responsible AI deployment requires. We already document assumptions, stress-test outputs, back-test against experience and stand behind our conclusions. Those habits do not need to be created from scratch for AI, they just need to be extended to it.
The AI handles the data processing, the pattern recognition, the first-draft generation. The actuary does what only an actuary can: exercises judgement, understands the investment and liability context, navigates the regulatory framework, and takes professional responsibility for the answer. That combination is considerably more capable than either alone – which is the whole point.
We have been running a survey of investment actuarial practitioners on their use of AI across the lifecycle. We will be honest: take-up has been disappointing. We know from the trade press, conference agendas and published research that AI tools are being actively deployed in investment decision-making – including at firms where actuaries work. Yet when we ask investment practitioners in the actuarial profession directly, the response has been largely silence.
We understand the instinct: professional sensitivities, firm confidentiality, reasonable uncertainty about what ‘using AI’ even means in a rigorous actuarial context. But the consequence is that the profession’s understanding of what is actually happening in practice remains thin, built more on vendor literature than on candid peer experience. We can do better than that.
If you are using AI in any part of the investment process – even experimentally, even just an LLM to speed up something you would have done anyway – we want to hear from you. Anonymous contributions are welcome. Incomplete experiences are welcome. Honest accounts of what did not work are, frankly, the most valuable of all.
The Investment Actuarial AI Working Party explores practical AI applications in investment practice across insurance, asset management, asset owners, pensions and consulting. Survey responses, case studies and new working party participants are all welcome. Contact us on LinkedIn.
Bhavana Maherchand (Chair), Nnamdi Odozi (Deputy Chair), Rahil Ram (Deputy Chair)
Arham Syed, Tiffany Lin, Htet Myet, Jamie Butler
1. Capital market assumption setting
AI-enhanced macro forecasting, yield curve modelling, credit spread prediction
2. Strategic asset allocation and ALM
Neural network proxy models for SCR, AI-driven ESG calibration, generative scenario generation
3. Portfolio construction
Regime detection, ML-enhanced optimisation, dynamic and liability-aware allocation
4. Implementation
NLP on earnings calls and filings, alternative data, credit signal generation
5. Risk monitoring
Early warning systems, regime change detection, continuous ORSA stress testing
6. Portfolio management and rebalancing
Automated drift monitoring, SCR consumption tracking, MA eligibility monitoring
7. Reporting and governance
Generative AI for investment commentary, consistency checking, regulatory disclosure
