Financial Advice New Zealand (FANZ) has officially opened nominations for its 2026 awards programme, with winners set to be announced at the National Adviser Conference from March 24 to 26. The event will draw advisers across wealth management, mortgages, and risk-based advice, including life, disability, and health insurance. Both individual advisers and advice businesses operating in life and health insurance, personal risk, mortgage and lending, and investment and retirement planning are eligible.
FANZ, New Zealand’s largest financial adviser-representative professional body for Financial Advisers and Financial Advice Providers (FAP’s), is positioning the conference not only as a celebration of industry excellence but also as a platform to discuss emerging regulatory and technological trends reshaping financial advice.
One of the most significant of these trends is the increasing role of artificial intelligence in insurance decision-making, which is prompting regulators to quietly recalibrate supervision. Rather than issuing prescriptive rules, authorities are moving toward a principles-based oversight model that places accountability squarely on boards and senior management. The shift signals a decisive rethinking of how AI-driven underwriting, pricing, and claims processes will be governed through 2026.
The Reserve Bank of New Zealand and the Financial Markets Authority have both warned that traditional compliance checklists are no longer sufficient for self-learning systems. AI models do not behave like static rulebooks, and regulators acknowledge that attempting to control them through rigid frameworks risks being ineffective or outdated. Instead, oversight is focused on governance, internal controls, and demonstrable technological understanding at the leadership level. Boards are now expected to show they understand how AI systems function, the risks they introduce, and how those risks are mitigated: a cultural shift for an industry long accustomed to delegating technical matters to specialists.
Regulators are particularly concerned about systemic risk. When multiple insurers rely on similar datasets or modelling techniques, errors or blind spots could propagate across the market, potentially amplifying losses during adverse events. To mitigate this, insurers are being asked to document model dependencies and run stress tests under correlated failure scenarios, ensuring resilience under volatile conditions, not just accuracy in stable markets.
Auditability has also emerged as a top priority. Regulators require that AI-driven decisions can be reconstructed and explained post hoc, especially in disputes over denied claims or sudden premium hikes. Insurers are encouraged to maintain detailed logs of model versions, training data, and decision pathways.
The shift toward outcomes-based supervision is evident across engagements. Rather than approving individual models, regulators are now assessing whether insurers have robust frameworks to manage AI throughout its lifecycle, from development and deployment to monitoring and retirement of models that no longer perform as intended. Accountability cannot be outsourced; even when insurers rely on third-party vendors for AI solutions, responsibility for outcomes remains with the licensed entity. This has prompted firms to renegotiate vendor contracts to include transparency and access to underlying model logic.
International developments are shaping local supervision. Regulators are monitoring frameworks emerging in Europe and the UK, where AI governance increasingly focuses on risk classification rather than technology specifics, and are adapting these approaches to New Zealand’s market context.
Automated claims handling is another area under scrutiny. While AI has improved operational efficiency, regulators insist that vulnerable customers must not be disadvantaged. Escalation pathways and human intervention must be in place to prevent hardship caused by automated decisions. Similarly, capital adequacy is under the microscope. AI-driven models could produce overly optimistic exposure estimates, particularly for climate-sensitive portfolios, prompting regulators to require conservative buffers where uncertainty is high.
Industry response has been mixed. Larger insurers with mature governance structures generally view the new supervisory approach as manageable, while smaller firms and insurtechs face steeper compliance challenges, needing to invest in documentation and controls that were not previously required. Regulators argue that early engagement is preferable to reactive enforcement, shaping governance expectations to prevent crises rather than respond after consumer harm occurs.
The evolving oversight model also reflects resource realities. Authorities cannot scrutinise every algorithm line by line, so the focus on accountability and governance aims to ensure insurers police themselves effectively. AI in insurance is no longer experimental and neither is its oversight. Firms that fail to adapt to the new supervisory playbook risk falling out of step in a market where trust, accountability, and technological sophistication are equally critical.
Against this backdrop of regulatory evolution and AI-driven change, the FANZ 2026 awards take on added significance as the programme highlights advisers and firms navigating the complexities of technology, governance, and compliance. As AI reshapes underwriting and claims processes, advisers who demonstrate innovation, ethical decision-making, and adherence to emerging supervisory expectations are likely to stand out. With 2026 unfolding, the relationship between insurers and regulators is set to become more collaborative but also more demanding. Innovation is encouraged, but only within frameworks that prioritise transparency, fairness, and resilience.