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Executive Summary: While insurance leaders plan to use machine learning capability in their pricing and underwriting processes, only a small proportion actually have embraced the technology. They need to take the next step because these are essential tools that will help them navigate changing regulations and be more responsive to consumer needs. Insurers must act now to be first movers, rather than reactive followers, writes Erez Barak, chief technology officer, Earnix, the software-as-a-service (SaaS) solution provider.

Artificial intelligence (AI) is one of the buzzwords circulating around the global insurance market in 2024. Some are excited by the possibilities it can bring, while others are opting for caution. But one train of thought is shared: it undoubtedly is an exciting and powerful tool that can benefit an organization – but it doesn’t have to be scary. Its efficacy and safety will come down to how it is implemented and governed within an organization.

Essentially, AI – if used effectively – can help speed up processes and increase accuracy, and it can be implemented in lots of different ways – for example, by powering machine learning capabilities.

Machine learning utilizes AI to make predictions based on big data and learned experiences. The result is a gradual automation of the insurance value chain, helping to remove manual effort from previously repetitive tasks – while helping insurers to realize tangible gains across risk, claims speed, and fraud prevention.

In terms of what ML offers, the positives far outweigh the negatives. Put simply, these complex algorithms are the new oracles of the insurance industry, predicting everything from who’s likely to file a claim to what premium consumers should pay. On the surface, it sounds like a win-win. When Earnix conducted its , 100% of insurance leaders said they plan to use machine learning (ML) in their pricing and underwriting processes. However, only 20% actually do so today.

Risk or Reward?

There are many reasons that insurers have been slow on the uptake of this new technology. A few of them include the difficulty of importing external technology into new ones, and the iterative and manual processes required to build generalized linear models, which help actuaries work out proposed rate changes.

In terms of implementing external technology, that’s an issue many companies face due to existing systems not being compatible with new technology. In the case of the iterative and manual processes, it requires having enough resources within the business, who also have experience, though AI and ML will help speed up that knowledge gap. AI plug-ins can also bridge the gap by ensuring legacy systems never stand still in terms of their capabilities.

This is a case of short-term pain for long-term gain. Ultimately, the primary motivation for the use of AI and ML in regulated industries like insurance is rooted in the need to constantly accelerate high-quality decision making. The faster you can make decisions, aided by ML, the more business you can capture, while providing a better service will positively affect customer satisfaction.

Another reason insurers have been slow to embrace the technology is the erroneous perception of the lack of flexibility in machine learning development due to software constraints connected to legacy systems.

The question for insurers is how can they participate in a new digital ecosystem that includes machine learning capability when their legacy systems are not built for it? The reality is that, while some legacy players have been slow to upgrade their systems, this hasn’t stopped new digital insurance offerings from spreading throughout the market, taking sizable shares in the regions they operate. The market is moving forward, and so must everyone who wants a slice of it.

Additionally, it takes time to see the value of new machine learning because it requires data input over a period of time. This is a question of both time and adequate resources. Most – if not all – platforms that are available to purchase and embed into insurers’ systems will come with all the relevant guidance and direct, virtual assistance.

Changing Industry Regulations

Changing regulation in the insurance industry requires new technology, and this is a fact that can’t be ignored. Earnix found that over one-third (38%) of respondents to its 2023 Industry Trends Report said that changing industry regulations will require them to consider new tools or technology. These tools include AI/ML, policy personalization to cover for a person’s specific needs, dynamic pricing rather than set costs per level of cover, and predictive analytics. So, while the insurance industry has been historically slow to adopt new technology, it finds itself at the crossroads of needing innovation to respond effectively to evolving regulatory requirements and shifting customer sentiment.

ML helps to increase accuracy. It peels back the layers of these complex systems to reveal the “why” and “how” of their decisions. In the insurance industry, this transparency is not just a matter of curiosity, it’s a matter of trust and fairness to customers.

For insurers, explainable machine learning can be the bridge between innovation and customer confidence. When customers understand how their data is used and why certain decisions are made, trust grows.

For instance, if a health insurance application is denied, a clear explanation can ensure the customer knows it’s not arbitrary but based on understandable factors such as business rules or regulatory strictures.

Explainability also returns a human oversight to an increasingly automated process. It allows insurance professionals to review and understand the machine’s recommendations, ensuring that they align with ethical and legal standards. This human oversight is crucial, as it ensures that ML aids, rather than replaces, human judgment.

Those who adopt ML now will also gain a competitive advantage in the marketplace, which could prove very beneficial. Å˽ðÁ«´«Ã½Ó³»­ companies that embrace these technologies early may capture market share, attract innovative talent, and differentiate themselves from competitors still reliant on traditional methods.

As previously stated, enhancing customer experience is vital, too. These models can personalize offerings, streamline processes, and provide faster responses to customer inquiries or claims, leading to higher satisfaction and retention rates.

The Time Is Now

Change is a process that requires time, patience and new learning. But there are riches on offer to those who choose to take the leap.

It’s important to say that insurers don’t have to go it alone – collaborating with established, reputable tech providers in the development and deployment of machine learning solutions could offer a strategic advantage. By leveraging external expertise and resources, insurance companies can accelerate innovation and mitigate implementation challenges.

So, amid changing regulations, technology enhancements aplenty and changing consumer expectation, ML can propel an insurer’s business to a focus and accuracy never seen before. And while there is plenty to consider and plan for, it’s mostly a question of confidence, which is the biggest roadblock to things we do – or don’t do – personally and professionally.

Topics Carriers InsurTech Data Driven Artificial Intelligence