Artificial intelligence promises many things to many industries, and the life insurance/annuity market is no different. Greg Winterton spoke to Alexandr Dudarev, CEO of Inshurik, an AI-driven insurance platform, to discuss a range of sub-topics at the intersection of AI and life/annuity carriers in this month’s Q&A.
GW: Alex, let’s start with lapse risk – this is a very applicable topic for LMI’s readers, given that it’s one of the biggest investment risks in the life ILS space. How is AI going to impact both carriers and investors here?
AD: I think AI can help transform lapse risk management from a passive risk assessment exercise into an active, real-time strategy for value retention. For both carriers and platforms, it enables dynamic persistency modelling; instead of only realising a policy has lapsed after the fact, or catching it during late-stage grace periods, AI algorithms can continuously scan early behavioural indicators, policy interactions, and broader microeconomic signals to flag lapse probability months before it occurs, which means that carriers can consequently act with personalised, automated interventions.
For life ILS investors, given that lapse risk directly impacts long-term yield and target IRR, the gain that investors can get from greater predictability of duration and payout schedules means a reduction in the tail risk associated with unexpected lapses. Ultimately, AI can give investors in the life ILS space greater confidence in cash-flow stability, which directly translates into tighter pricing, lower cost of capital, and expanded capacity for funding transactions across the entire life ILS ecosystem.
GW: The heavily intermediated customer acquisition infrastructure in the US life insurance market means that there is something of an accessibility floor in terms of cost. Tell us how AI can help life/annuity carriers cast a wider net.
AD: AI breaks this bottleneck in a couple of ways. First, AI fundamentally reimagines customer engagement; by deploying autonomous, multi-channel AI agents capable of handling complex prospect interactions 24/7, carriers, through platforms, can triage, qualify, and nurture leads at a fraction of the cost of traditional contact centres or manual outreach. These AI-driven workflows absorb the labour-heavy preliminary phases of sales, allowing human advisors to step in only when high-value consultation is required.
Second, this collapse in acquisition friction directly transforms policy economics and premium pricing. When acquisition and service costs drop significantly, carriers no longer need to price high fixed overhead into every contract, which means that they can efficiently underwrite smaller face-amount products, micro-annuities, or simplified-issue coverage that were previously unprofitable to distribute.
I’d also suggest that AI doesn’t necessarily displace the advisor ecosystem because it acts as a force multiplier by automating early-funnel engagement and optimising cross-sell workflows. It enables carriers to expand their total addressable market, offer competitive premium structures, and sustainably serve the massive protection gap without sacrificing margin or capital efficiency.
GW: What about the bulk purchase annuity (BPA) market? Is there the potential for AI to impact activity and pricing in this space at all, and if so, how?
AD: Absolutely. In fact, the BPA and pension risk transfer (PRT) market is uniquely poised for AI transformation because of the pure volume and complexity of data involved in these transactions. Traditionally, one of the biggest bottlenecks in bringing a BPA deal to market is the initial deal-triaging and data-sanitisation phase. Defined benefit pension schemes can have decades of fragmented, unstructured member data, so the cleaning, matching, and underwriting that data traditionally takes weeks or months, which inherently limits the number of transactions an insurer can price concurrently.
AI accelerates deal capacity and pricing velocity; advanced natural language processing and machine learning tools can digest, standardise, and reconcile raw scheme data quicker, enabling carriers to respond to quotes faster, evaluate more deals simultaneously, and reduce some of the administrative load built into bid preparation.
Second, AI enables insurers to layer non-traditional predictive datasets, macro-health signals, and socio-economic indicators on top of standard actuarial tables to gain a far more precise view of baseline longevity risk and mortality trend variance. When you can model mortality curves with higher fidelity, you reduce the required capital buffers and margin for uncertainty, leading to tighter, more competitive pricing for pension schemes without compromising carrier solvency.
GW: Inshurik is also active in the wearable medical tech space. How far advanced are these tools in terms of being able to better underwrite mortality and what’s your view on the challenge of getting these devices used at any kind of scale that can be beneficial to a life insurer?
AD: The real barrier to scaling these tools across a broad insured population isn’t the technology itself, it’s adoption and perceived consumer value.
The most common model at the moment is that a carrier offers policyholders a small premium discount in exchange for handing over continuous health tracking. But for most consumers, that value exchange simply isn’t compelling enough to drive daily, long-term habit change.
But in the later life, pre-care home segment, the immediate value is personal safety, independence, and peace of mind and when devices provide direct, immediate value to the user, adoption friction reduces. For life and annuity carriers, early detection of falls or rapid functional decline allows for rapid assistance or medical intervention, directly reducing acute mortality events and extending healthy longevity, and continuous monitoring generates structured, real-time health data that enables carriers to model late-life mortality curves with unprecedented precision, refining reserve requirements and improving post-retirement product pricing.
I’m not sure that wearables will scale across life insurance as generic wellness gizmos, but they can scale as specialised, life-enriching safety tools designed for specific life stages.
GW: Lastly, Alex, back to the capital markets. What’s the bull case for AI being a tailwind for increased activity in the life ILS space?
AD: A few things here. First, AI drastically shortens the diligence and origination cycle. Historically, onboarding a life ILS portfolio required institutional investors to review dense, non-standardised policy databases across varying jurisdictions. AI-driven data extraction and automated risk analytics convert months of manual underwriting audits into days. AI enables life ILS funds and institutional asset managers to deploy capital much faster and evaluate a significantly higher volume of sponsor opportunities. This increased speed in underwriting should reduce the cost of new trades hence allow the ILS market to target smaller opportunities.
Second, it elevates risk transparency and secondary market liquidity. One headwind for life ILS adoption has been the perception among capital market investors that underlying life liabilities are difficult to price continuously over time. Advanced machine learning models can provide continuous, real-time portfolio telemetry, tracking macro-health signals, persistency trends, and population mortality variances dynamically. When investors have high-confidence, real-time risk visibility, rather than static annual actuarial reviews, secondary trading spreads narrow, price discovery improves, and overall market liquidity expands.
Third, it unlocks novel collateral and structuring frameworks. AI allows sponsors and ILS managers to slice and tranche complex biometric risks and insurance risks with a high degree of precision.
Life ILS has always been an attractive asset class for institutional investors due to its low correlation with traditional financial markets, but transaction volume has historically been constrained by friction, structural complexity, and illiquidity. AI can directly dismantle these structural barriers.
Alexandr Dudarev is CEO of Inshurik
