AI’s role in Aotearoa’s changing insurance landscape

AI algorithms can sift through vast amounts of new and historic claims data to identify irregularities that may signal fraudulent activity, enabling insurers to mitigate losses stemming from deceptive claims. As AI is trained with more data and diverse scenarios, its accuracy in detecting fraudulent patterns improves over time and will reach unprecedented review speeds and accuracy.

AI has significant potential to improve the customer experience, for example with the development of chatbots and tools to facilitate more efficient handling of customer queries. At claim time, AI can help streamline the claims handling process by automating processes, leaving claims managers more time to focus on the needs of the claimant. These efficiencies can make the customers feel more connected to their insurance company, ultimately leading to improved customer satisfaction and retention.  

As climate change takes hold, weather claims are increasing in volume. AI has the ability to triage claims, passing complex claim decisions requiring judgment to humans, but automating the processing of BAU claims or claims under a certain dollar threshold to reduce or avoid processing backlogs and claims leakage. Not only does this result in a quicker claim payout for the customer, improving experience; but it could reduce insurers’ costs and could transform significant event response times and management. 

An area where machine learning can add significant value is the use of predictive models to identify ways to underwrite policies more efficiently by accurately assessing the risk posed by each potential customer. Through customer segmentation and clustering models, insurers can gain a deeper understanding of customer risk profiles, which can help the insurer make more informed pricing decisions; reduce cross-subsidisation, and support strategic risk diversification. 

AI is already being leveraged to detect the risks of future extreme events and isolate higher-risk geographic locations, by predicting extreme weather or other natural hazard events. Better information and predictions allow for action to reduce the potential damage these events cause. AI can also be used to predict future trends, such as areas at increased risk of flooding or bushfires; or identify areas where managed retreat is the best option. AI could also analyse historic event responses and identify areas for response improvement; areas that went well, and how past event responses can inform future ones.

AI can also bring about significant technological advancements in the field of healthcare. Improvements in medical diagnostic equipment through the use of AI will have flow-on effects on insurers who sell health insurance. Other industries, such as transportation and construction, will start seeing improvements in safety and monitoring, leading to fewer workplace accidents. Insurers need to maintain awareness of these trends to adapt quickly to maintain their competitive advantage.

AI will assist in helping insurers understand their markets better through data mining and analysis. Customer, competitor, and global insurance insights will be more readily available than ever. 

Establishing the unique AI value proposition is recognising and realising the transformation AI can bring across core business processes and operational efficiency. 

The AI Strategy should look at and respond to the organisation’s biggest problems and include room for experimentation and ongoing innovation. The strategy requires a clear roadmap with timelines, milestones, and initiative owners to give all the organisation’s stakeholders visibility on the journey ahead. When developing the roadmap, remember the motto 'Think big, start small, and scale quickly’. 

The strategy will also need to consider AI governance, and keep track of upcoming developments in the AI regulatory space to ensure AI usage compliance. 

To support the strategy, insurers will also need to get their data in order. Effective AI implementation and successful data management go hand-in-hand. If your data is not ready for AI, your business is not ready for AI. 

Stakeholders will need to see the benefits and return on investment before, during, and after AI implementation. It is business-critical to quantify the realised benefits and calculate the return on AI investment. However, measuring benefits is often easier said than done, and close to impossible if not taken into consideration early in the process. For a successful AI journey, key KPIs and measuring mechanisms need to be established in conjunction with the initial AI roll-out.