Modernizing the Insurance Industry with AI and Machine Learning

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Read more about author Adam Goran.

Insurance is a massive expense for many people. According to a report by the Association of British Insurers, the average cost for comprehensive car insurance across the U.K. is £460 per year. And for drivers under 25, the cost can jump even higher. Health insurance in the U.K. can be costly too – according to one report, the average premium for U.K. private health insurance is £1,435 per year. 

With people spending so much of their income on various insurance policies (home, car, contents, pet, liability, to name but a few), the insurance industry must modernize to better serve its customers. 

For those in the insurance industry, customer-centric design from the outset and the implementation of artificial intelligence (AI) and machine learning (ML) are critical to updating the customer experience or forging, new, personalized, and timely insurance models. Read on for tips and best practices for modernization.  

End-to-End Customer Centricity Model 

For an industry that people are required to buy into, putting the customer first can fall by the wayside. People generally only remember their insurance when they have to renew their policy or when they are in the unfortunate position of needing to file a claim. Traditionally, a customer would go online to find the cheapest deal and move ahead with securing that – a model that has no customer centricity; not to mention the brokers, call centers, and other time-consuming measures a customer has to go through to file a claim. However, the customers’ needs, more than ever, must come first, and insurance companies must adapt to ensure their products and services put those needs first. This means that companies should design new products that are centered on customer needs and provide succinct, easy, and logical journeys at all points of the customer lifecycle.

When switching to a customer-centric design and a positive customer experience, the insurance industry should consider a self-serve model all the way through the lifecycle. There are several benefits to this model, including allowing the customer to make insurance decisions on their own timeline and reducing both time and cost spent on securing insurance. This will not eliminate the need for insurance agents; instead, those agents need to be equipped with the right tools (video conferencing abilities, etc.) to serve customers when needed. 

Beyond the purchasing stage, new models are being forged for coverage. For example, invisible insurance is taking hold across several industries. In the auto industry, invisible insurance refers to the practice of including one week of auto insurance with the purchase of a vehicle. This eliminates the need for customers to hunt for insurance before purchasing a vehicle, and it increases the likelihood that after the first week, they’ll stick with the insurance they received when they bought the car to save themselves the time and difficulty of searching for new insurance. The concept of invisible insurance puts the customers’ needs at the forefront by eliminating the pain point of searching for insurance. 

VitalityHealth in the U.K. is an exceptional example of an insurance brand keeping the customer at the forefront. VitalityHealth, which specializes in health insurance, has started giving customers an Apple watch to help track their activity and wellbeing. This practice allows the customer to “touch” the brand every single day, creating a more meaningful and personalized connection. It also allows VitalityHealth to collect data to help adjust the pricing of their health insurance plans – a benefit to the consumer. By putting the customer at the center of its model, it’s ensuring brand loyalty to an industry that notoriously lacks brand awareness. 

The questions insurance experts should ask themselves when considering modernizing the insurance buying processes or forging a new model for insurance include: Is it feasible? Is it viable? Is it desirable? When putting the customer at the forefront from the very beginning, and by reflecting on the possibility, viability, and desirability of the solution, insurance experts can modernize a dated industry and build customer loyalty at the same time. 

The Benefits of AI and ML 

It’s impossible to explore the digital transformation and modernization of the insurance industry, without considering technology like AI and ML. Fraud is a huge issue in the insurance industry: According to the Federal Bureau of Investigation, the total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. That means insurance fraud costs the average U.S. family between $400 and $700 per year in the form of increased premiums.

AI and ML can be used to identify potential fraud, giving insurance companies an operational advantage that they have historically lacked by not modernizing. By catching fraud earlier in the process, money and resources are not spent on frivolous claims – lowering insurance costs for legitimate consumers. It is, however, still critical to allow for human intervention if special attention is needed for a particular customer. 

It can also help with the creation of timely and personalized pricing and policies.

As insurance firms increasingly use AI and ML in their practices, they must remember to keep the customer at the forefront. Data privacy when using these technologies can be a major concern for customers, and insurance companies need to be upfront (and have the evidence) that customer data is being utilized in ethical and trustworthy ways. 

When it’s time to modernize (and the time is now!) insurance experts should keep the customer at the forefront of any new design or insurance model; doing so will make customers more loyal to the brand. And when implementing new technologies like AI and ML to fight against fraud in the industry, experts must remember to keep customers well informed. 

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