Innovation issues affecting the insurance industry

The innovation environment is in constant motion. We asked Sam Ketley, Head of Enterprise Risk Solutions at Aon New Zealand, to share a few innovation matters currently affecting the insurance industry. 

Parametric insurance 

With the increasing risk of natural disasters, there is the potential for traditional indemnity-based insurance to become either limited or, in the worst case, unavailable.   

What is parametric insurance? 

Parametric insurance is a simple, straightforward, and fast-paying risk transfer solution triggered by a specific, pre-defined event. It provides clients with easy-to-understand ‘if-then’ coverage. If a specific pre-defined event occurs (such as a natural catastrophe or weather event), as determined by independent, third-party data sources, the insured then unlocks a highly flexible source of risk capital. The broad coverage and quick settlement mechanics (where payment is available within weeks of an event) can help clients mitigate uncertainty and provide access to liquidity to accelerate recovery after an event. 

How does parametric insurance work? 

Parametric insurance is a risk transfer instrument based on the growing amount of data available to describe risk events. It is particularly well-suited for natural catastrophes but can also be applied to other emerging risks where data is available. 

Unlike traditional insurance, which would typically be triggered directly by losses associated with the risk event, parametric insurance is instead triggered by the data that describes the event. Given the real-time nature of data availability associated with these events, parametric insurance allows faster and more efficient payouts without requiring time-consuming and often subjective assessments.  

Additionally, parametric insurance can be customised to meet the needs of organisations, offering maximum flexibility as clients seek to enhance their resiliency. 

What does parametric insurance cover? 

Parametric coverages are available for a large and growing list of perils. These include hurricanes, tropical cyclones, and earthquakes; secondary perils such as severe convective storms, hail, tornado, flood, and wildfire; climate and weather perils such as extreme temperatures, excess rainfall, or snow, as well as operational concerns such as river height. There are also applications for emerging perils such as non-physical damage, business interruption, cyber, cloud outages, supply chain issues, and risks associated with climate change and transition. 

Generative artificial intelligence (AI) 

Key points:  

  • Generative AI is a type of AI that can create material such as images, music, or text. It is already a proven disruptor, and its adoption is growing at an explosive rate. 
  • The insurance market's understanding of generative AI-related risk is in a nascent stage. This developing form of AI will impact many lines of insurance, including technology errors and omissions (E&O) / cyber and professional liability. 
  • AI presents significant opportunities but also introduces new risks. Organisations should work with experts to set policies and establish governance frameworks that align with regulatory requirements and industry standards. 

As generative AI changes the way companies do business, it is creating new risks and causes of loss that impact both the companies and their business partners, such as third-party vendors and digital supply chains. Recent events and court cases highlight the developing forms of risks associated with generative AI, including copyright, trademark and patent infringement, discrimination, and defamation.  

Bearing in mind the important difference in the risks – and risk management approaches – associated with model creation versus model usage and different approaches, some examples in this emerging risk field include: 

  • Data privacy and confidential information 
  • Unreliable model training 
  • Unintended AI actions 
  • IP / confidential information / trade secrets 

Insurance market trends 

The insurance market's understanding of generative AI-related risk is in a nascent stage. This developing form of AI will impact many lines of insurance, including technology E&O / cyber, professional liability, media liability, and employment practices liability, depending on the AI’s use case. Insurance policies can potentially address AI risk through affirmative coverage, specific exclusions, or by remaining silent, which creates ambiguity. 

Insurers are defining their strategies around this rapidly changing risk landscape, including: 

  • Clarifying coverage intent / addressing "silent AI coverage" through revised policy language related to AI risk. 
  • Building out their underwriting requirements, which are already very robust. While underwriters are just starting to ask questions, the process may become burdensome and prolonged with the many potential applications that could be created and deployed. 
  • Developing creative AI products and solutions (e.g. a leading insurer has developed a product that provides a performance guarantee based on an AI risk assessment). 
  • Expanding their technology-based talent competencies, either organically or through partnerships and / or acquisitions, to support underwriting and pricing through technical assessments and monitoring. 

Managing AI risk 

While the productivity gains of generative AI are easily recognisable, organisations should take great care and conduct regular risk assessments as they embrace this new world. Aon suggests that they work with their insurance broker, technology experts, lawyers, and consultants to set policies and establish a governance framework aligning with regulatory requirements and industry standards. Regarding organisations' use of AI, some components of that framework may include: 

  • Routine audits of AI models to ensure that algorithms or data sets do not propagate unwanted bias. 
  • Ensuring an appropriate understanding of copyright ownership of AI-generated materials. 
  • Developing and implementing this same framework into a mergers and acquisitions checklist. 
  • Mitigating risk through the implementation of B2B contractual limitation of liability, including vendor risk management. 
  • Insertion of human control points to validate that the governance model used in the AI’s development aligns with legal and regulatory frameworks.
  • Conducting a legal, claims and insurance review and considering alternative risk transfer mechanisms in the event the insurance market begins to avoid these risks. 

Bloomberg Research forecasts the generative AI market will grow to $1.3 trillion over the next 10 years, up from $40 billion in 2022. As firms race to share in that growth, they would do well to stay focused on the potential risks and issues that will arise along the journey. 

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This article was brought to you in paid partnership with Aon New Zealand.