How AI is transforming insurance

Applications for AI in insurance span a range of operational areas, from pricing and underwriting through to customer interactions and claims processing. We break down how AI is being applied in key areas, where we’re seeing the biggest developments and what’s next for insurers.

Boosting efficiency, accuracy and possibility in underwriting

AI-led improvements in underwriting efficiency and practices are helping insurers boost sales opportunities by reducing the turnaround time for quotes, and improve risk assessment to support better pricing and profitability. In personal lines, where most customers purchase directly from an insurer, AI helps reduce the number of questions required in quote forms and, in certain cases, pre-fills questions. United States insurer State Farm and local player Suncorp, for example, use machine learning applied to geospatial images to streamline home insurance quoting and tie characteristics of the property to potential losses.

For commercial lines, where much of the underwriting process is manual, AI helps rapidly extract relevant information from documents. In the US, Liberty Mutual is demonstrating the benefits by applying natural language processing to documents to quickly extract relevant information to underwriters. This supports their conversations with brokers and customers, and halves the time required to extract loss data for mid-size and large accounts.

More streamlined, effective claims assessment

Image recognition helps simplify and streamline claims assessment, and supports enhanced digitisation of customer experience. This includes Aviva UK’s use of property and motor vehicle damage images to estimate repair costs, and IAG Firemark Venture’s recent investment in Ravin AI, an Israeli tech start-up. Ravin AI produces automated motor vehicle damage and repair cost estimates, which are based on customers’ mobile phone images.

Moving beyond image recognition, natural language models, including large language models, are helping to extract relevant information from claims forms and notes. This increases the efficiency and effectiveness of claims teams, who can focus their time on claims requiring closer human review. For example, United Kingdom insurer RSA is applying this technology to claims from its pet insurance portfolio, automating the reading and extraction of relevant data from medical reports, treatments and progress notes.

Chatbot makeovers for enhanced customer experience

Most people are familiar with the artificial intelligence chatbot. Following recent advancements in large language models, such as those underpinning ChatGPT, the chatbot has benefitted from significantly enhanced capability and performance. Insurers employ these not only as direct-to-consumer instant messaging platforms to answer simple enquiries but also as ‘in house’ assistants to customer service centres.

“AI supports conversations with brokers and customers, and halves the time required to extract loss data for mid-size and large accounts.”

US-based Allstate Insurance’s ‘Amelia’, for example, leads call-centre employees through step-by-step procedures to help answer a variety of customer questions. Amelia also ‘listens’ to interactions she doesn’t understand, to expand her knowledge. Allstate says the benefits of Amelia include a reduction in the time taken to train new employees, and she’s also helping employees better comply with industry regulations.

Innovations amid a warming world and a focus on bias

With predicted investment in AI growing to $200 billion worldwide by 20251, we’re going to continue to see innovations in the insurance field. Budding research into AI for disaster prediction, management and relief is especially relevant for insurers in a warming world, with more frequent and intense weather events. In particular, researchers are developing frameworks that combine pre-disaster images with weather data and trajectory of hurricanes. These provide rapid insights on damage caused by natural disasters, with potential to assist in allocating resources for assessment, repair and disaster relief.

Another strand of research looks to address potential bias and inequities introduced by AI pricing models. Discrimination-free pricing, for example, seeks to produce pricing models that avoid direct or indirect discrimination based on protected features such as gender, while maintaining overall model performance. This has been motivated in part by European Union regulation banning pricing discrimination based on protected attributes. We expect bias and fairness to become a focus of pricing models more globally, as insurers respond to rapidly developing government regulations and directives on the use and application of automated decision processes.

Exciting times ahead, but risk management key

While insurers have a lot to be excited about with the current and emerging uses of AI, it’s also a time to ensure systems and processes help them navigate the challenges posed by its use. AI processes can be brittle, exposing insurers to financial, regulatory, legal and reputational risks. Examples include financial losses driven by ‘rogue’ underwriting or pricing algorithms, legal penalties from breaches to customer privacy requirements and loss of goodwill from inequitable treatment of customers.

Insurers will need to ensure their risk management practices are appropriately structured to incorporate, manage and mitigate AI-specific risks and provide a firm basis for meeting increasing regulatory and compliance requirements. These include impending government regulation on artificial intelligence and significant expansion of requirements under the Privacy Act. By expanding the legislation, the government aims to capture a much broader range of data-related practices, and increase customer rights to control how their data is used and stored.

This article first appeared in RADAR FY2023, Taylor Fry’s annual roundup of Australia’s insurance landscape.