Algorithmic economy for insurers

Here’s Part II in my series on AI and insurance coverage. Today’s focus: how insurers are reshaping underwriting, claims handling, and product design as AI becomes a central driver of both operational risk and systemic exposure. The industry continues to adapt as AI adoption accelerates, combining analytical rigor, product innovation, and a deeper focus on operational risk. Underwriting now incorporates AI-specific governance assessments. Carriers evaluate controls such as human-in-the-loop oversight, versioning, documentation standards, data lineage, model-update procedures, and vendor-management rigor. These factors serve as indicators of “AI risk maturity,” influencing pricing, retentions, and coverage terms. With limited historical loss data, insurers increasingly pair governance scoring with scenario-based modeling. Stress tests simulate failures in widely used third-party AI tools to understand correlated losses and systemic exposure, guiding reinsurance and portfolio strategies. As I’ve reported, new policy forms are being developed to address AI-driven exposures more directly, including: • Standalone AI liability policies covering flawed outputs, operational disruption, reputational harm, contractual performance failures, and regulatory exposure. • Excess liability wraps to address gaps created by AI exclusions in legacy programs. • First-party AI incident response coverage for BI, rep impact, recall-type expenses, and investigation costs tied to AI malfunctions or model drift. Cyber and E&O programs also use modular AI endorsements to adapt traditional coverage without creating silent exposures. AI-related claims increasingly require cross-disciplinary expertise. Claims teams may work with data scientists, ML engineers, and forensic analysts to review model artifacts, decision logs, training data, and prompt histories. The focus is often on reconstructing failure modes, bias, drift, prompt injection, or misconfiguration, rather than on malicious acts. Because AI deployments often involve internal teams, vendors, and integrators, liability analysis may span multiple contributors and require coordination with regulators. Aggregation risk is real. When multiple insureds rely on the same foundational models or third-party AI services, a single failure can trigger correlated losses. To manage this, insurers use vendor-concentration analysis, scenario stress-testing, and layered risk-sharing structures, including reinsurance mechanisms designed for tail-risk events. But not all carriers are expanding coverage. Some lines, particularly D&O and certain E&O segments, have introduced exclusions for losses arising from AI use or development. What This Means • Expect underwriters to require transparency and governance discipline. • Standalone AI coverage can fill gaps but may include scrutiny and sublimits. • Effective risk management requires strong governance, vendor oversight, and documentation. #AI #cyberinsurance

"This paper focuses on developing a conceptual blueprint for AI insurance that addresses unintended outcomes resulting directly from an AI system's normal operation, where outputs fall within the declared scope but diverge from intended behaviour. Such failures are already silently embedded in existing insurance portfolios, neither affirmatively covered nor excluded, and thus remain unpriced and unmanaged. We argue that dedicated AI insurance is necessary to quantify, price, and transfer these risks, while simultaneously embedding market-based incentives for safer and more secure AI deployment. The paper makes four contributions. First, we identify the core underwriting challenges, including the lack of historical loss data, the dynamic nature of model behaviour, and the systemic potential for correlated failures, and propose mechanisms for risk transfer and pricing, such as parametric triggers, usage-based coverage, and bonus-malus schemes. Second, we examine market structures that may shape the development of AI insurance and highlight technical enablers that support the quantification and pricing of AI risk. Third, we examine the interplay between insurance, AI model risk management, and assurance. We argue that without insurance, assurance services risk becoming box-ticking exercises, whereas underwriters, who directly bear the cost of claims, have strong incentives to demand rigorous testing, monitoring, and validation. In this way, insurers can act as guardians of effective AI governance, shaping standards for risk management and incentivising trustworthy deployment. Finally, we relate AI insurance to adjacent coverage lines, such as cyber and technology errors and omissions." Lukasz Szpruch Agni Orfanoudaki Carsten Maple Matthew Wicker Yoshua Bengio Kwok Yan Lam Marcin Detyniecki AXA

AI in insurance is not a productivity hack 🚫 Automating the past is safe and will generate marginal returns. The real value lies in underwriting the future! AI is being talked about everywhere in insurance. Too often, the conversation stalls at efficiency theatre. Faster underwriting. Cheaper claims handling. Fewer people doing more work. Useful, but small. The real opportunity sits elsewhere. Reimagining Risk in an AI-Driven World, developed by the International Insurance Society, captures this shift well. Having contributed to the report and led the executive workshop in Zurich, one message came through very clearly: the next decade will separate insurers making marginal improvements from those rebuilding their operating models around new forms of risk, data, and human judgement. AI is not the strategy. It is the unlock 🔓 The strategic upside is not incremental. It sits in: • New insurable risks emerging from intangible assets, cyber, AI, and climate • Proprietary knowledge graphs, data, decision systems become a true edge • Human judgement being augmented, not replaced, in a trust-based industry • Governance, talent, and data strategy becoming board-level differentiators, not IT issues 🤩 One stat should give leaders pause. Nearly 90% of firms are experimenting with GenAI, yet only around a quarter have anything in real production. Plenty of motion. Limited transformation. That gap is not about technology. It is about operating model courage. Keen to hear from peers across insurers, reinsurers, brokers, MGAs, and insurtechs: • Where have you seen AI move the needle beyond efficiency? • What is genuinely blocking scaled deployment? • Are we underwriting new risks fast enough, or just automating old ones? If insurance gets this right, we don’t just adapt to an AI-enabled world. We become one of its core stabilisers. Thoughts and counter-views welcome. Full report link in comments 👇 Anders Malmström, Joshua Landau, Colleen McKenna Tucker

🌟 The ground just shifted beneath the world of risk! And most leaders missed it. Here is why...💫 Did you see this? Last week, Munich Re began insuring AI model errors for mortgage lenders. While this certainly demonstrates that AI is becoming a more prominent emerging risk in our lives, it also signals a seismic shift: the #AgenticFrontier is no longer a theoretical future—it has arrived. For years, we've talked about transformation. Yet Boston Consulting Group (BCG)'s data shows a stark reality: while 78% of P&C insurers are “dabbling” with AI in the claims process, only 4% have successfully scaled it. Imagine what this means across the insurance operations and the overall enterprise. The rest are caught in the “pilot trap,” a sinkhole for laggards. The gap between the talkers and the doers has become a chasm. The 4% are fundamentally redesigning their businesses around AI. This is no longer about whether you'll embrace #agenticAI. It's about how you'll lead the transformation. For corporate leaders, the mandate is clear. For founders, the 18-month enterprise sales cycle is now optional for those who can provide de-risked, insured solutions. Here is the playbook for those ready to move from ambition to action: 1️⃣ Stop the science projects. Pick one end-to-end process—claims, underwriting, finance, customer support—and commit to a complete, AI-driven redesign. The real ROI is in redesigning the unglamorous, high-impact back-end operations, not bolting AI onto broken workflows. 2️⃣ De-risk your transformation. AI error insurance is now a board-ready mandate. Use it to turn AI from a high-risk experiment into a scalable, enterprise-grade asset. 3️⃣ Reframe the protection gap as an innovation mandate. The same creativity used to insure algorithms must be turned toward insuring humanity against Nat Cat/ extreme weather risks and other systemic risks. This is the largest market opportunity of the next decade. The uninsurable world is a choice, not a necessity. The leaders of 2026 will be those who use the tools of the agentic frontier to rewrite the rules of risk. What is the most fundamental “gap” you see in your organization’s AI strategy right now? Please share... Is it the tech, the talent, or the trust? And enjoy this week's newsletter. 👏🏽 #CapacityGap #TrustbyDesign

I am pleased to announce that my current research is pivoting toward a fundamental challenge fot he industry: the impact of predictive AI on the very soul of the insurance contract- Alearity. In my forthcoming work, "The Death of Aleatory: Insurance in the Age of Algorithmic Determinism", I explore how the transition from "chance" to "data determinism" compels us to rethink core legal doctrines across both Civil Law and Common Law jurisdictions: 1.- The Validity of the Contract: Does a contract become void for lack of "insurable risk" when an algortihm can predict a loss with a near-absolute certainty? 2.- The inversion of Uberrima FIdes How does the duty of utmost good faith evolve when insurers possesse more data about the risk than the policyholder? 3.- From Indemnity to Prevention. The structural shift from ex-post compensation to an ex-ante prevention model. the challenge we face is not merely technological; it is deeply legal and ethical. How do we preserve the social principle of mutual aid when the "shroud of uncertainty" is lifted by Big Data? The digital revolution does not merely represent a change in the tools of insurance; it signifies a tectonic shift in tis legal ontology. As we have explored, the transition from probabilistic uncertainty to algorithmic determinism threatens to render the classical “aleatory” contract obsolete. However, this “death of aleatory” should not be viewed solely as the end of insurance, but as its metamorphosis. The law must evolve to recognize a new category of contract: the Preventive Insurance Model. In this new paradigm, the insurer´s primary obligation shifts from the ex-post indemnification of a fortuitous loss to the ex-ante mitigation of a predicted one. The challenge for future regulators and jurists -including those within the framework of the European IA Act and Global Common Law- is to ensure that this precision does not destroy the social contract of solidarity that underpins insurance. We must prevent a future where only those whose “uncertainty” is still high can afford protection, while those with “predicted certainty” are left in a legal and financial vacuum. The law must preserve a “sphere of legal uncertainty” to protect the human right to a future that is not pre-written by an algorithm”. #UniversidadPontificiaComillas #ICADE #InsuranceLaw #Artificialintelligence

The AI boom is increasingly driving macroeconomic and insurance risks   •Disconnect between demand and supply impacts on the economy - The US economy is exposed to large, concentrated investment and asset-price effects, while productivity and output gains emerge only slowly. Households are vulnerable to a loss of AI optimism: a sustained drop in the heavily AI-driven S&P 500 could wipe out trillions of household net worth and cut consumer demand.   • New assets, shifting demand - For insurers, AI is already expanding the universe of insurable assets. Investment in data centers, power infrastructure, and always-on digital operations is creating new exposures across property, engineering, liability, and specialty lines. In the near term, this supports insurance demand. Over time, however, AI is also likely to disrupt industries, reducing risk exposures in some sectors while creating new ones in others. The result may be a reallocation of insurance demand rather than uniform growth, increasing the importance of portfolio steering and risk selection.   • New risks, complex interdependencies - AI introduces emerging risk dimensions that do not fit neatly within traditional insurance boundaries. These include cyber and fraud risks, liability exposures linked to algorithmic failures or bias, intellectual property disputes, and non-physical business interruption. Growing reliance on a small number of cloud and AI service providers adds a further layer of systemic and accumulation risk.   • At the same time, insurers themselves are adopting AI to enhance underwriting, claims, and operations, bringing efficiency gains but also reinforcing the need for strong governance, transparency, and human judgement.    Our latest sigma insight discusses the risks and opportunities of AI adoption:   https://lnkd.in/eXb22JRA   #ArtificialIntelligence, #Macroeconomics, #InsuranceRisk, #FinancialStability

Autonomous underwriting is already changing the economics of the insurance industry. On the latest AI Heroes & Headaches, I spoke with Alex Schmelkin, Founder and CEO of Sixfold. His message to CEOs, CIOs, and underwriting leaders was clear: this shift is operational, measurable, and already reshaping competitive advantage. Two years ago, underwriting agents sounded theoretical. Today, Sixfold is completing full underwriting cycles faster and more consistently than human teams. The companies that adopt these systems are gaining margin, speed, and accuracy. The companies that delay are losing pricing power. Resistance in the boardroom has flipped. The new question is how to deploy AI Agent faster than competitors. Buying access to OpenAI is not a strategy. If you cannot tie AI to defined business outcomes, you are not transforming. You are spending. The leaders who win follow a very simple order of operations: - Desired Outcomes. - Data & AI Strategy. - Execution through iteration. If you lead underwriting, pricing, or risk and believe autonomous systems are still years out, this episode will challenge that assumption. It may change your 2026 roadmap. Full episode in the comments. 🎧 #AI #AIForAll #AIHeroesAndHeadaches #AgenticEnterprise #AgenticTransformation