Regulatory pressure is becoming a central factor shaping the insurtech sector as it continues to mature. According to an article published in FinTech Global, compliance, transparency, and capital discipline are emerging as defining forces influencing innovation and operational strategy across the industry in 2026.
The article draws on perspectives from several leaders in the insurtech ecosystem, who identified governance, regulatory frameworks and capital requirements as key challenges shaping the year ahead.
Governance Built Into Technology Systems
Simha Sadasiva, co-founder and CEO of Ushur, said regulatory readiness must be integrated directly into technology architecture rather than addressed solely through compliance teams.
“Compliance, data privacy, and how you underwrite risk and interact with customers are all key governance pillars in insurance,” Sadasiva said in the FinTech Global article.
Sadasiva explained that insurers should embed governance safeguards directly into their technology stacks. These safeguards can include deterministic risk prediction models, detailed audit trails that document how automated decisions are made and fallback mechanisms that ensure human involvement when necessary.
According to Sadasiva, regulated industries require systems that are verifiable and auditable. As regulatory scrutiny increases, the ability to demonstrate that technology systems are trustworthy, traceable, and controllable has become increasingly important when working with enterprise clients.
AI Governance and Transparency Requirements
The FinTech Global article also identified artificial intelligence governance as a major regulatory focus area for insurers.
Ido Deutsch, chief revenue officer at Producerflow, said insurers must address governance requirements related to data privacy, model management, and risk oversight when deploying AI systems.
“With AI, there is a lot of governance required around data privacy, managing models and understanding risk,” Deutsch said in the article. “Many of these models are almost like black boxes.”
Deutsch explained that the complexity of many AI models can create challenges for insurers operating in regulated markets where decision-making must be transparent and explainable.
To address this issue, insurers may need to ensure that automated processes remain auditable and that the rationale behind automated decisions is clearly documented.
Deutsch said insurers are likely to adopt hybrid operating models in which automation manages many backend processes while human oversight remains part of regulated decision-making.
“Humans will have to be in all those processes, maybe fewer humans, but doing different things,” he said.
Capital Regulation and IFRS 17 Challenges
While technology governance is an important focus, the FinTech Global article also highlighted capital regulation as a structural challenge for insurers.
Yasser Rajwani, product manager at Earnix, pointed to solvency requirements and IFRS 17 as areas where regulatory frameworks may not fully reflect the capabilities of modern insurance technology.
Advances in data and analytics allow insurers to price policies at the individual policyholder level and evaluate risk with greater precision than when many existing regulatory frameworks were developed.
However, prudence margins and capital requirements have not always adjusted to reflect these advancements.
Rajwani noted that capital efficiency becomes especially important in higher-interest-rate environments. Holding excess capital can limit reinvestment flexibility and constrain growth.
“Most insurers want to get their capital requirement down to the last decimal,” Rajwani said in the article.
Developing Legal Frameworks for Generative AI
The article also discussed the regulatory questions surrounding generative AI in the insurance sector.
Peter Ohnemus, president and CEO of dacadoo, said regulators are working to keep pace with rapid technological developments.
“The problem is that regulators are a bit overwhelmed, because everything moves so fast,” Ohnemus said in the FinTech Global article.
Ohnemus said explainability is particularly important for life and health insurers because automated decisions in those sectors can affect customer well-being.
According to Ohnemus, insurers must be able to document how generative AI models operate, explain how recommendations are generated, and show how outputs are produced.
He also called for clear legal frameworks and structured oversight to guide the use of generative AI.
“If we play football together and we have clear rules, we have more fun and become more successful,” Ohnemus said. “If it’s anarchy and no rules, it becomes dangerous.”
Ohnemus added that the insurance industry traditionally favors structured governance models, as it relies on long-term trust and regulatory accountability.
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