AI ’requires some creative thinking and, more importantly, some practical application to make it useful,’ says managing director
When considering artificial intelligence (AI), some immediately think of the classic 80s sci-fi film Terminator, in which an advanced, hostile AI system takes over the world and attempts to eliminate humans entirely.
However, the proper considerations should be around understanding of AI’s practical capabilities and how they can be deployed.
That was according to Toby MacLachlan, managing director of Ignite, who told Insurance Times that AI has the potential to automate tasks and allow brokers to dedicate more time to other aspects of their roles.
He explained: ”It is a question of understanding what it is and what it can do. It just requires some creative thinking and, more importantly, some practical application to make it useful.”
AI technology has become an important consideration in the insurance industry, with more firms starting to explore its uses.
For example, a number of insurers have implemented AI into businesses models. This includes Zurich, which said it was experimenting with the chatbot to overhaul its claims processing, and insurtech Artificial Labs, which followed suit with the launch of a ChatGPT pilot in May last year (2023).
MacLachlan explained that, in practical terms, AI can simplify complex tasks in the sector, including pricing.
He explained: ”If you take very practical examples of it, [the industry] can see how it works without it being scary.
”For example, motor pricing is [based on multiple factors] such as location, age, credit score and more.
”However, combining all those factors is really hard. It’s not something a human brain can do perfectly well. You need these tools called the machine learning.”
Machine learning is a strand of AI that enables computers to learn from data and improve their performance on tasks without being explicitly programmed manually.
Machine learning tools can enable precise analysis, enhancing the ability to adjust pricing dynamically based on feedback, ultimately impacting profitability positively without a significant deviation from set parameters, noted MacLachlan.
The future?
When it comes to implementing AI into business models, MacLachlan cautioned that the industry should refrain from ”releasing it to the public” until it’s truly ready.
Read: Underwriters express concern about slow progress in underwriting processes
Read: Risks associated with AI and big data becoming more of a concern – Axa
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His comments echoed those of Nicholas Robert, emerging risks modeller at Lloyd’s, who previously told Insurance Times that the industry needed to consider “mitigating the risks” when exploring how “rapidly changing technologies can be applied”.
Robert explained: “When implementing AI, with the potential to transform processes and even entire sectors, it’s important to encourage an experiment-led approach where technologies can be piloted in a controlled environment to ensure efficiency, which can reduce the risks before they’re used more widely.”
And Catherine Carey, head of marketing at Consumer Intelligence, warned that it was important for the industry to consider AI’s “impact on its customers”.
Carey explained that, during “vulnerable” moments such as car accidents or home emergencies, respondents did not perceive AI as the ideal option.
She said: “While there’s a considerable industry focus on how AI can address these concerns, it’s crucial to first consider the impact on customers and how they will experience these changes.
“It’s essential that, when incorporating AI, the industry does the right thing by thinking about AI from the consumer perspective.”
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