Summary: Insurance industry leaders are planning to use machine learning capabilities in pricing and underwriting processes, but only a handful have actually adopted the technology. Insurers need to take the next step because these are essential tools to keep up with changing regulations and better serve consumer needs. Insurers need to act now to be pioneers, not reactive followers, writes Erez Barak, chief technology officer at Earnix.
Artificial Intelligence (AI) is one of the buzzwords in the global insurance market in 2024. Some are excited by the possibilities that AI brings, while others are cautious. However, one belief is common: AI is undoubtedly an exciting and powerful tool that can benefit your organization, but it doesn't have to be scary. The effectiveness and safety of AI depends on how it is implemented and managed within your organization.
In essence, AI, if used effectively, can help speed up processes and improve accuracy and can be implemented in a variety of ways, including enhancing machine learning capabilities.
Machine learning leverages AI to make predictions based on big data and learned experience, gradually automating the insurance value chain and removing manual labor from previously repetitive tasks while helping insurers realize tangible benefits in terms of risk, claims speed, and fraud prevention.
When it comes to what ML has to offer, the positives far outweigh the negatives. Simply put, these complex algorithms are the new prognosticators of the insurance industry, predicting everything from who is likely to file a claim to how much premium consumers should pay. On the surface, it seems like a win-win. When Earnix conducted its 2023 Industry Trends Report, 100% of insurance industry leaders said they planned to use machine learning (ML) in their pricing and underwriting processes. However, only 20% are actually doing so today.
Risk or reward?
There are many reasons why insurers are slow to adopt this new technology, some of which include the difficulty of onboarding external parties to new technology and the repetitive, manual process required to build generalized linear models that help actuaries calculate proposed rate changes.
When it comes to adopting external technologies, it is an issue many businesses face as their existing systems are not compatible with the new technology. Repetitive manual processes require well-experienced resources within the business, but AI and ML can help fill the knowledge gap. AI plugins can also bridge the gap by ensuring that legacy systems do not become stagnant.
This is a case of long-term gain without short-term pain. After all, the primary motivation for using AI and ML in regulated industries such as insurance is the constant need to accelerate quality decision-making. The faster you can make decisions with the help of ML, the more business you will win and the better service you will provide, positively impacting customer satisfaction.
Another reason insurers are hesitant to adopt this technology is the mistaken perception that software constraints associated with legacy systems make them inflexible when it comes to developing machine learning.
The question for insurers is: how can they participate in the new digital ecosystem that includes machine learning capabilities when their traditional systems are not machine learning enabled? The reality is that while some traditional players have been slow to upgrade their systems, nothing can stop new digital insurance offerings from spreading across the market and capturing a significant share in the regions where they operate. The market is moving forward, and anyone who wants to get a piece of it must move forward.
Additionally, it takes time to realize the value of new machine learning because it requires data entry over a period of time. This is both a matter of time and adequate resources. Most (if not all) of the platforms that can be purchased and embedded into an insurer's system come with all the associated guidance and in-person virtual assistance.
Changes in industry regulations
Regulatory changes in the insurance industry require new technology, and that's a fact that cannot be ignored. Earnix found that more than a third (38%) of respondents to its 2023 Industry Trends report said regulatory changes in their industry will require them to consider new tools and technologies. These tools include AI/ML, personalization of policies to cover an individual's specific needs, dynamic pricing rather than fixed costs per coverage level, and predictive analytics. Thus, while the insurance industry has historically been slow to adopt new technologies, it is at a crossroads where it needs to innovate to effectively respond to evolving regulatory requirements and changing customer sentiment.
ML helps improve accuracy; peeling back the layers of complex systems to reveal the “why” and “how” of 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 trust. how Their data will be used, why Certain decisions are made and trust is developed.
For example, if an application for health insurance is denied, a clear explanation can ensure a customer that it was not arbitrary but based on understandable factors such as business rules or regulatory constraints.
Explainability also brings human oversight back into an increasingly automated process: insurance professionals can review and understand the machine's recommendations and ensure they comply with ethical and legal standards. This human oversight is crucial because it ensures that ML supplements human judgment, rather than replacing it.
Adopting ML now will be extremely beneficial as it will also provide a competitive advantage in the marketplace: insurers that are early adopters of these technologies may be able to gain market share, attract innovative talent, and differentiate themselves from competitors that still rely on traditional methods.
As mentioned earlier, improving customer experience is also key: these models allow you to personalize service, streamline processes, and respond quickly to customer inquiries and complaints, thereby increasing satisfaction and retention.
The time is now
Change is a process that takes time, patience, and new learning, but it is richly rewarding for those who choose to take the plunge.
Importantly, insurers don't have to go it alone. Working with established, reputable technology providers in the development and deployment of machine learning solutions can provide a strategic advantage. By leveraging external expertise and resources, insurers can accelerate innovation and mitigate implementation challenges.
So, amidst changing regulations, significant technology enhancements, and shifting consumer expectations, ML can propel insurance companies’ businesses to unprecedented focus and precision. There is a lot to consider and plan for, but it is primarily a question of confidence, which is the biggest obstacle to what we do or don’t do personally and professionally.
topic
Insurance companies Insurtech Data-driven artificial intelligence
