maddaisy – cultivating innovation

Tag: financial-services

  • Insurtech’s AI-Fuelled Five Billion Dollar Comeback — And the Question the Industry Has Not Answered

    Global insurtech funding reached $5.08 billion in 2025, up 19.5% from $4.25 billion the year before. It is the first annual increase since 2021 — and, according to Gallagher Re’s latest quarterly report, it marks a fundamentally different kind of recovery from the one the sector last enjoyed.

    The 2021 boom was driven by venture capital chasing consumer-facing disruptors. The 2025 comeback is driven by insurers and reinsurers themselves investing in operational AI. That distinction matters far more than the headline number.

    The money is coming from inside the house

    In 2025, insurers and reinsurers made 162 private technology investments into insurtechs — more than in any prior year on record. This is not outside capital speculating on disruption. It is the industry itself funding its own modernisation, a shift Gallagher Re describes as a “changing of the guard” in the insurtech investor community.

    The fourth quarter was particularly striking. Funding hit $1.68 billion — a 66.8% increase over Q3 and the strongest quarterly figure since mid-2022. More than 100 insurtechs raised capital for the first time since early 2024, and mega-rounds (deals exceeding $100 million) returned in force, with 11 such rounds totalling $1.43 billion for the full year, up from six in 2024.

    Property and casualty insurtech funding rebounded 34.9% to $3.49 billion, driven by companies like CyberCube, ICEYE, Creditas, Federato, and Nirvana, which collectively secured $663 million in Q4 alone. Life and health insurtech, by contrast, declined slightly — a 4.6% dip that underlines where the industry sees its most pressing operational gaps.

    Two-thirds of the money follows AI

    The most telling statistic in the report is this: two-thirds of all insurtech funding in 2025 — $3.35 billion across 227 deals — went to AI-focused firms. By Q4, that share had climbed to 78%.

    Andrew Johnston, Gallagher Re’s global head of insurtech, frames this as convergence rather than a trend: “Over time, we see AI becoming so integrated into insurtech that the two may well become synonymous — in much the same way as we could already argue that ‘insurtech’ is itself a meaningless label, because all insurers are technology businesses now.”

    That trajectory is visible in the deals themselves. mea, an AI-native insurtech, raised $50 million from growth equity firm SEP in February — its first external capital after years of profitable organic growth. The company’s platform, already processing more than $400 billion in gross written premium across 21 countries, automates end-to-end operations for carriers, brokers, and managing general agents. mea claims its AI can cut operating costs by up to 60%, targeting the roughly $2 trillion in annual industry operating expenses where manual workflows persist.

    At the seed stage, General Magic raised $7.2 million for AI agents that automate administrative tasks for insurance teams — reducing quote generation time from approximately 30 minutes to under three in early deployments with major insurers.

    Profitability, not just growth

    What separates the 2025 wave from the 2021 boom is that several insurtechs are now proving they can make money, not just raise it.

    Kin Insurance, which focuses on high-catastrophe-risk regions, reported $201.6 million in revenue for 2025 — a 29% increase — with a 49% operating margin and a 20.7% adjusted loss ratio. Hippo, another property-focused insurtech, reversed its 2024 net loss with $58 million in net income, driven by improved underwriting and a deliberate shift away from homeowners insurance toward more profitable lines.

    These are not unicorn-valuation stories. They are companies demonstrating operational discipline — the kind of results that explain why insurers and reinsurers, rather than venture capitalists, are now leading the investment.

    The B2B shift

    Gallagher Re’s data reveals another structural change worth watching. Nearly 60% of property and casualty deals in 2025 went to business-to-business insurtechs — a 12 percentage point increase from 2021’s funding boom. Meanwhile, the deal share for lead generators, brokers, and managing general agents fell to 35%, the lowest on record.

    The implication is clear: capital is flowing toward technology that improves how existing insurers operate, not toward new entrants trying to replace them. The disruptor narrative of the early 2020s has given way to something more pragmatic — and, arguably, more durable.

    This parallels a pattern visible across financial services. As maddaisy noted when examining Lloyds Banking Group’s AI programme, established institutions are increasingly treating AI not as an innovation experiment but as core operational infrastructure — and measuring it accordingly.

    The question the industry has not answered

    For all the funding momentum, Johnston raises a challenge that the sector has yet to confront seriously: the “so what” problem.

    “As the implementation of AI starts to deliver efficiency gains, it is imperative that the industry works out how to best use all of this newly freed up time and resource,” he writes.

    This is not a hypothetical. If mea can genuinely reduce operating costs by 60% for a carrier, that frees up a substantial portion of the 14 percentage points of combined ratio currently consumed by operations. The question is whether that freed capacity translates into better underwriting, deeper risk analysis, and improved customer outcomes — or whether it simply gets absorbed into margin without changing how insurance fundamentally works.

    The broker market is already feeling the tension. In February, insurance broker stocks dropped roughly 9% after OpenAI approved the first AI-powered insurance apps on ChatGPT, enabling consumers to receive quotes and purchase policies within the conversation. Most analysts called the selloff overdone — commercial broking remains complex enough to resist near-term disintermediation — but the episode illustrated how quickly market sentiment can shift when AI moves from back-office tooling to customer-facing distribution.

    What to watch

    The $5 billion figure is a milestone, but the real signal is in its composition. Insurtech funding is no longer a venture capital bet on disruption. It is the insurance industry’s own investment in operational AI — led by incumbents, focused on B2B infrastructure, and increasingly backed by profitability rather than just promise.

    Whether that investment translates into genuinely better insurance — not just cheaper operations — depends on how the industry answers Johnston’s question. The money is flowing. The efficiency gains are materialising. What the sector does with them will determine whether this comeback is a lasting structural shift or just the next chapter of doing the same things with fewer people.

  • Lloyds Banking Group’s £100 Million AI Bet: What the UK’s First Agentic Financial Assistant Means for Enterprise AI

    Lloyds Banking Group expects its artificial intelligence programme to deliver more than £100 million in value this year — double the £50 million it attributes to generative AI in 2025. The figures, disclosed alongside the group’s annual results in January 2026, represent one of the more concrete attempts by a major financial institution to attach a number to what AI is actually worth.

    That specificity matters. As maddaisy examined last week, PwC’s 2026 Global CEO Survey found that 56% of chief executives still cannot point to measurable revenue gains from their AI investments. Lloyds is not claiming to have solved the ROI puzzle entirely, but it is doing something most enterprises have not: publishing the numbers and tying them to specific operational improvements rather than vague promises of transformation.

    The financial assistant: what it actually does

    The headline initiative is a customer-facing AI financial assistant, which Lloyds describes as the first agentic AI tool of its kind offered by a UK bank. Announced in November 2025 and scheduled for public rollout in early 2026, the assistant sits within the Lloyds mobile app and is designed to help customers manage spending, savings, and investments through natural conversation.

    The system uses a combination of generative AI for its conversational interface and agentic AI to process requests and execute actions. In practical terms, a customer can query a payment, ask for a spending breakdown, or request guidance on savings options — and the assistant will interpret the request, plan the necessary steps, and carry them out. Where it reaches the limits of what automated support can handle, it refers users to human specialists.

    The scope is intended to expand. Lloyds has said the assistant will eventually cover its full product suite, from mortgages to car finance to protection products, serving its 28 million customer accounts across the Lloyds, Halifax, Bank of Scotland, and Scottish Widows brands.

    Testing at scale, not in a lab

    Before public launch, Lloyds tested the assistant with approximately 7,000 employees, who collectively completed around 12,000 trials. That is a meaningful pilot — large enough to surface edge cases and failure modes that a controlled lab environment would miss, and conducted with users who understand the bank’s products well enough to stress-test the system’s accuracy.

    The employee testing sits alongside a broader internal AI deployment that has already delivered measurable results. Athena, the group’s AI-powered internal search assistant, is used by 20,000 colleagues and has reduced information search times by 66%. GitHub Copilot, deployed to 5,000 engineers, has driven a 50% improvement in code conversion for legacy systems. An AI-powered HR assistant resolves 90% of queries correctly on the first attempt.

    These are not experimental pilots. They are production tools used at scale, and the fact that Lloyds is willing to attach specific performance metrics to each one distinguishes its approach from the many enterprises that describe AI impact in qualitative terms only.

    The ROI question: credible or convenient?

    The £100 million figure invites scrutiny, and it should. “Value” in corporate AI disclosures is notoriously slippery — it can mean cost savings, time savings converted to a monetary equivalent, revenue uplift, or some combination of all three. Lloyds has not published a detailed methodology for how it arrived at the £50 million figure for 2025 or how it projects the 2026 target.

    That said, the bank’s approach has features that lend it more credibility than many comparable claims. The internal tools have named user populations and specific performance benchmarks. The customer-facing assistant was tested with thousands of employees before launch, not unveiled as a concept. And the 2025 figure is presented as a delivered outcome, not a forecast — a distinction that matters when most enterprises are still struggling to prove any return at all.

    Lloyds also rose 12 places in the Evident AI Global Index last year — the strongest improvement of any UK bank — suggesting that external assessors see substance behind the claims.

    Agentic AI in financial services: the governance dimension

    The move to customer-facing agentic AI in banking raises governance questions that go beyond what internal productivity tools require. As maddaisy explored earlier this week, Deloitte’s 2026 AI report found that only one in five enterprises has a mature governance model for agentic systems. When those systems move from internal search assistants to customer-facing financial advice, the stakes escalate considerably.

    A banking AI that can execute transactions, provide savings guidance, and eventually handle mortgage queries operates in regulated territory. The Financial Conduct Authority’s expectations around suitability, fair treatment, and clear communication apply regardless of whether the advice comes from a human or an algorithm. Lloyds has acknowledged this by building in human referral pathways, but the real test will come at scale — when millions of customers interact with the system simultaneously, and edge cases multiply.

    Ron van Kemenade, the group’s chief operating officer, has framed the launch as “a pivotal step in our strategy as we continue to reimagine the Group for our customers and colleagues.” Ranil Boteju, chief data and analytics officer, has positioned it as a demonstration of responsible deployment, noting that the assistant “can understand and respond to specific, hyper-personalised customer requests and retains memory to offer a more holistic experience, ensuring the generated answer is safe to present to customers.”

    What this signals for the sector

    Lloyds is not the first bank to deploy AI, nor the first to make bold claims about its value. What distinguishes this move is the combination of a concrete financial baseline (£50 million delivered), a named and tested product (the financial assistant), a clear expansion roadmap (full product suite), and an institutional commitment to upskilling (a new AI Academy for its 67,000 employees).

    For practitioners watching the enterprise AI landscape, the Lloyds case offers a useful reference point against the prevailing narrative of deployment fatigue and unproven returns. It does not resolve the broader ROI question — one bank’s results do not establish an industry pattern — but it does suggest that organisations which invest in specific, measurable use cases and test rigorously before launch can move beyond the proof-of-concept purgatory that still traps most enterprises.

    The harder question is what happens next. An AI assistant that helps customers check spending patterns is useful. One that advises on mortgages and investment products enters a different category of risk and regulatory complexity. How Lloyds navigates that expansion — and whether the £100 million value target holds up under the scrutiny of real-world deployment — will be worth watching over the months ahead.