Fintrade Securities Corporation Ltd (FSCL), an investment and financial advisory firm, has highlighted the growing influence of artificial intelligence (AI) in New Zealand’s insurance sector.
The firm noted that AI, along with machine learning and data analytics, is now integrated into key insurance functions, including underwriting, claims processing, and customer service.
Traditionally, New Zealand insurers have relied on manual workflows and legacy systems.
The adoption of AI-driven tools is enabling companies to process information more efficiently and handle larger data volumes.
Rather than replacing employees, these technologies are designed to complement the workforce by automating repetitive tasks and supporting decision-making.
Underwriting, a core insurance process, has been notably affected.
In the past, underwriters assessed risk based on historical data and professional experience.
FSCL observed that AI now allows for a more data-driven approach, utilising machine learning models that analyse a wider range of variables, such as behavioural trends, geographical data, and economic indicators.
“With AI, underwriting becomes faster, more consistent, and far more precise. Machine learning algorithms trained on years of policy and claims data can assess new applications by drawing on subtle correlations in behaviour, geography, economic indicators, and even seasonal patterns. These models continuously learn and improve, adapting to emerging risks and market behaviours,” said an insurance broker and financial advisor at FSCL.
Property insurers, for example, are using AI to combine satellite imagery and weather data for dynamic risk assessment.
In the auto sector, telematics data enables pricing based on actual driving behaviour.
Health insurers are also leveraging data from wearable devices to personalise coverage and identify potential risks.
The insurance industry’s dependence on documentation has led to the adoption of intelligent document processing (IDP).
By combining optical character recognition (OCR) and natural language processing (NLP), insurers can digitise and interpret both structured and unstructured documents.
This reduces manual data entry, minimises errors, and accelerates claims and policy issuance.
NLP tools help insurers extract relevant information and understand context from customer communications, which streamlines operations and improves service delivery.
As a result, insurers can process claims more quickly and reduce administrative workloads.
AI is also enabling insurers to move beyond traditional demographic-based pricing.
Access to real-time behavioural data allows for more personalized products and pricing models.
Usage-based insurance, for instance, sets premiums according to individual driving patterns or lifestyle choices.
Additionally, AI systems can provide policyholders with proactive risk alerts, such as notifications about severe weather or property maintenance recommendations.
This shift from reactive claims handling to preventive risk management is changing how insurers engage with customers.
Despite the increased use of automation, human professionals continue to play a vital role.
Underwriters and claims assessors apply judgment and empathy in complex cases.
According to FSCL, AI tools are intended to support, not replace, human expertise.
By automating routine tasks, insurers can focus more on customer service and strategic planning.
The industry is moving toward a hybrid model, where technology and human skills work together.
This approach aims to improve productivity and reduce burnout in administrative roles.
The integration of AI is prompting insurers to invest in data science, cloud infrastructure, and employee training.
As workflows evolve, there is a greater emphasis on data governance and ethical AI practices.
Insurers are required to ensure transparency, fairness, and accountability in automated decision-making, especially in areas such as policy eligibility and fraud detection.
New Zealand’s regulatory environment is adapting to these changes, with increased attention to the use of customer data and the need for explainable algorithms.
Insurers are expected to maintain human oversight and regularly audit AI models for bias.
Recent research by recruitment and advisory firms showed that AI is becoming a regular feature in New Zealand workplaces, although most employees have not received formal training.
A survey of white-collar workers found that while optimism about AI is high, only a minority have participated in employer-led training.
Most AI adoption is employee-driven, with general-purpose tools being the most widely used.
Public sentiment toward AI in insurance remains mixed. While consumers recognise benefits such as faster service and improved efficiency, concerns persist about data privacy, transparency, and the fairness of automated decisions.
