Insurers face AI risk from over-reliance on few vendors
Moody’s has raised concerns about the dangers of over-reliance on a limited number of artificial intelligence providers, noting that insurers could experience widespread operational failures if key cloud or foundation-model suppliers encounter major disruptions. The credit rating agency’s alert arrives as financial institutions—especially insurers and banks—speed up their adoption of AI to streamline essential functions like claims processing and credit evaluation.
Over 75% of financial firms in the UK already deploy AI systems, the Treasury select committee report states, with insurers at the forefront of the transition to digital tools. Moody’s, however, points to a significant vulnerability in this rapid expansion: many firms are becoming overly dependent on a narrow group of vendors, which creates a concentration risk. A service outage or pricing adjustment by a dominant provider could trigger cascading effects across industries, while proprietary data and contractual terms may offer only minimal safeguards.
Why Rigidity Poses the Biggest AI Risk
The analysis reveals a deeper structural challenge for insurers. While AI can enhance operational efficiency, over-reliance on vendor-controlled systems may restrict flexibility when technological or market conditions evolve. Tim Hardcastle, co-founder and CEO of INSTANDA, argues that the solution lies not in slowing innovation but in embedding adaptability into AI deployments from the outset.
“Moody’s warning lands at an important moment,” Hardcastle tells FinTech Magazine. “Insurers have moved quickly to adopt AI, and rightly so, but speed only pays off if the architecture can flex as fast as the technology changes. The real risk isn’t AI; it’s rigidity. Too many operations are still built around a small number of vendors, models, or platforms that are difficult to unpick without significant cost or disruption.”
Insurers have moved quickly to adopt AI, and rightly so, but speed only pays off if the architecture can flex as fast as the technology changes. Hardcastle’s argument is particularly relevant as insurers move AI from experimentation into underwriting, pricing, claims, distribution, and operational decision-making. If these processes become dependent on one model provider, cloud environment, or software architecture, replacing that technology may become expensive and disruptive. “Adaptability must be designed in from the start, not bolted on after a warning such as this one,” Hardcastle adds. “That means insurers retaining genuine control over their core systems, so they can test, replace, or recalibrate the technology and data underneath their products without waiting on a vendor’s release cycle.”
How INSTANDA Builds Flexible AI Systems
INSTANDA positions its software as a configurable digital insurance platform built around a single global platform and codebase. The company says this architecture is intended to reduce technical debt while allowing insurers to adapt products, workflows, and distribution models at the client level.
Among INSTANDA’s recent AI-driven tools are Quote Policy Assist, which automates insurance quotes; INSTANDA MAX, engineered to handle large volumes of complex commercial policies; and INSTANDA CLEAR, which converts manual processes, such as email-based underwriting, into structured digital workflows. The company also prioritizes compatibility through open APIs, enabling insurers to incorporate third-party solutions while maintaining operational control.
Hardcastle details concrete steps insurers can adopt to reduce vendor concentration risks while advancing AI integration. These include maintaining ownership of core systems and proprietary data, evaluating multiple AI models rather than depending on a single supplier, and including resilience provisions in vendor agreements. For lower-risk tasks, smaller specialized models can be deployed, while ongoing oversight ensures AI outputs remain trustworthy and verifiable.
