While a traditionally stagnant industry in regards to digital transformation of processes, the insurance sector has seen more rapid innovation in the last few years, as firms look to get ahead amidst intensifying competition. As well as evolving culture within, and indeed beyond the business to bolster partnerships, insurance technology (insurtech) capabilities underpinned by artificial intelligence are now widely available to procure across the market.
With insurance-facing AI capabilities predicted to reach global market value totalling almost $80bn by 2030, there is much room to grow regarding discovery of new value add, use cases and revenue streams for insurers. Here are some valuable examples of artificial intelligence innovation in insurtech, as well as the present risks to consider, and how insurance firms can go about maintaining success with the technology.
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Key use cases
Point of claim
Point of claim remains the truest and most vital aspect of the customer journey, when it comes to providing policyholders with positive engagement and experience. AI can optimise that initial customer interaction with the insurance firm, by delivering assets of organised data sets on-demand to help insurers make the right decision instantly, in real-time.
“The key is to deliver an efficient outcome in a simple way,” said Samuel Knott, sales director UKI at Fadata. “Utilising AI can reduce claims processes and deliver seamless end-to-end, straight-through processing with no stress. AI accelerates the entire claims process by automating mundane, routine tasks and by using predictive analytics to speed up the decision making process.
“AI also minimises errors in claims processing, resulting in more accurate payouts, while detecting suspicious patterns to flag fraudulent claims.”
Experience orchestration
As insurers look to become more customer centric, the coupling of AI with advanced analytics can help provide a more specific, personalised and real-time picture of insurance customers.
With insurance customers coming to rely on online platforms for purchasing and managing their policies for such a particular commodity, interactions with the firms themselves are few and far and between, which can water down the user experience. However, experience orchestration — the leveraging of customer data and AI by insurance companies to create highly personalised interactions — can be implemented to improve relations long-term.
Manan Sagar, global head of insurance at Genesys, explains: “For example, instead of navigating a generic call tree when inquiring about a claim, customers could be greeted with a proactive and specific response based on their unique situation. This approach not only improves the customer experience but also enhances employee efficiency by automating tasks or routing calls more effectively.
“As the insurance industry navigates the digital age, experience orchestration can serve as a powerful tool to uphold the tradition of trust and personal relationships that have long defined the industry. Through this, firms can differentiate themselves in an increasingly commoditised market and ensure their customers remain loyal and satisfied.”
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Chatbots
A common outlet for optimisation of insurance claimant interactions are AI-powered chatbots. Since the advent and subsequent model evolution around generative AI, chatbot technology has become more customisable, allowing for more accurate alignment with business goals.
“AI is helping insurtechs finally unlock the true potential of chatbots. In theory, chatbots are a great idea, but to date they have not been hugely helpful — often reaching an impasse if the question does not perfectly match up with a pre-determined set of answers,” said Scott Logie, chief commercial officer at Sagacity.
“Using AI to scan internal documents and trusted outside sources, insurtechs are able to broaden the scope, arming chatbots with much more relevant information, and delivering results in a style and tone of voice that matches the brand.
“This gives a much better experience across the insurance industry, improving chatbots so much that some customers will believe they are speaking to a human.”
Code review and training using ChatGPT
In addition, large language model-powered software such as ChatGPT is being used to review code for capabilities, as well as facilitate faster examination of documents, and augmenting skill building for staff.
“We tested whether ChatGPT could pass a technical whiteboard test. It wasn’t perfect but it did a pretty good job, which raises a potential use case: quickly reviewing and documenting code,” said Tom Chamberlain, vice-president of customer and consulting at hyperexponential.
“In a similar vein, it could scan legal documentation like contracts and simplify them to, for example, quickly pull out the most critical legal parts and explain them. Or it could compare a contract from the previous year’s to identify any changes.
“One of the more exciting use cases I’ve heard for ChatGPT is using it to help teach and upskill a workforce in complicated actuarial or insurance topics, for example explaining what a generalised linear model is.”
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Risks to consider
While avenues for artificial intelligence innovation in the insurtech space are plentiful, with room for new use cases set to grow, the technology still carries risks that insurance firms must consider when it comes to development and deployment.
Generative AI privacy
Maintaining and staying on top of required data privacy protocol remains paramount when managing AI models, especially given the sensitive client information that insurtechs need to access.
Regarding the above use cases he identified for ChatGPT in the insurtech space, Chamberlain added: “All these use cases require something ChatGPT isn’t yet able to deliver: total privacy.
“ChatGPT is open, and so today you must be careful what you put into it. Generative AI solutions that can promise total privacy and security could be a game changer for the industry.
“It’s incredible watching how people are using ChatGPT, and there’s no doubt it can be used to help the world of insurers, but only if used responsibly and in areas that are relevant.”
Compliance considerations
The issue of data privacy when deploying and managing AI systems leads us to the need to stay compliant in line with regulations globally. Indeed, the issue of privacy remains as much a legal one as it is ethical.
The insurance industry has been heavily regulated for a long time, and insurers will also need to comply with evolving national and international legislation like the new AI Act in the EU, which will take some time to navigate as firms become accustomed to the new regulations.
“One of the biggest challenges for insurtech firms adopting AI is navigating the regulatory landscape,” said Knott. “Insurtechs must assure accurate, relevant, complete and consistent high-quality data. AI is only as good as underlying data sets. If data isn’t up to standard, it can be detrimental to insurers.
“Imagine missing values, outdated information or irrelevant data in the insurance industry! It would skew analysis, lead to incorrect predictions and conclusions, and all-in-all undermine the reliability of AI generated insights.”
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Over-reliance
Additionally, depending too heavily on AI without human oversight can lead to staff getting lost in the data, while decisions made go unexplained when it comes to determining how best to tackle future queries.
“Human expertise is essential for handling complex cases and making nuanced decisions that AI may struggle with. It’s like letting a GPS guide you blindly without checking road conditions,” said Rajeev Gupta, co-founder and chief product officer at Cowbell Cyber.
“For example, a multinational corporation with diverse operations and industries makes it unique. The AI might not have the context or the ability to assess the broader implications outside its training data.
“It’s crucial to remember that AI operates within predefined boundaries. If those boundaries don’t encompass the complexity of certain requests, there’s a risk of overlooking critical factors.”
Ensuring long-term success
Reducing data silos
With the pros and cons of AI investment in mind, insurance firms should firstly ensure that siloed data is brought together, as much as possible without breaching needed security measures.
“Creating a deeper understanding of the customer and business across a shared platform that is kept up-to date and offers modernised data capabilities, can unite a company and help it to achieve goals and cohere values,” said Knott.
“This can often require a cultural shift as many organisations prefer to implement competition between departments to achieve results. But facilitating a ‘work-together’ attitude can streamline processes, reduce siloed workflows, and much more to the benefit of the broader enterprise, especially as insurers look to unite internally to become more customer-centric in their business.”
Risk management
Once in place, AI can help to determine whether decisions made throughout the insurance provision process, and triage possible risks while saving insurers time otherwise spent on administrative duties.
“The AI can categorise quotes, for example using a traffic light system; a green message meaning ‘Yes’, Amber meaning ‘needs looking at’, and red meaning ‘no’,” said Chamberlain.
“The more you feed into the algorithm confirming or correcting decisions, the better the algorithm becomes. However, it doesn’t talk back to you.”
Deploying intelligent automation
Finally, it is important to note that AI is not a magic wand that will solve all problems for an insurance company. It is best used as a tool for employees to utilise, and much like a human staff member, an artificial intelligence model can and will make errors.
Piers Williams, global insurance sales manager at AutoRek, argues that “while there is a lot of hype in the corporate world regarding AI’s potential, it is likely still several years until we see tangible solutions leveraging AI to support back and middle office finance operations processes”.
Williams continued: “Due to its ability to supplement information, and learn from the data that feeds it, AI can come to its own conclusions, which when viewed in the context of a financial control processes is not needed.”
Instead, according to Williams, insurers should currently be prioritising investment in intelligent automation over AI.
“Intelligent automation also typically follows a set of pre-defined rules, rather than attempting to simulate human thoughts or spot patterns,” he says. “If you adopt AI to make decisions in outcomes where data doesn’t conform, then you introduce the risk of AI error in place of human error.”
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