What Hidden Data Can Do for the Insurance Business


Like so many other industries, insurance is becoming increasingly data-driven. Data, of course, has always been an important resource for decisions on claims, risk, and coverage. But in a data-driven scenario, the data becomes the main focus of that decision-making process, with artificial intelligence, machine learning, and other advanced analysis technologies mining and refining data, enabling companies to make more cost-effective, efficient, and target decisions.

Data- especially that collected from sensors and other IoT devices that are multiplying by the day—along with AI could be doing much more for insurance companies. According to experts, AI systems utilize only a small amount of all data available; as much as 90% of collectable data goes to “waste.”

This often hidden data is collected from sensors, IoT devices, cameras, and other sources. Smart homes – where almost everything, from lights to refrigerators to washing machines, is connected – are increasing in popularity; modern vehicles are essentially moving computers, with a plethora of sensors gathering information on almost every aspect of the driving experience and environment; and wellness device apps record data on health, activity, exercise, and lifestyle. Almost all this data is voluntarily provided by users, as part of their user agreements – and much of it goes unused, simply because it’s unstructured.

But in fact, this collected data from the real world could be structured and entered into databases, and companies could analyze it with advanced AI- and machine learning-based systems that can help them avoid overpayment, fraud, and other issues that skew the cost of insurance, providing them with insights that will ensure that companies – and customers – see the best possible outcomes.

With advanced data collection and analysis, insurance companies can save money, eliminate inefficiencies, offer better and more relevant products, and ensure that they offer the right products to the right customers. That data can be used to set risk, determine premiums, develop products, triage claims, prevent fraud, enhance customer loyalty, and decide on what markets to target. Using unstructured data, companies will be able to develop insights that are as exact as possible – far more accurately than is currently possible.

And it can benefit customers as well. With improved data collection and analysis, companies will be able to process claims much more efficiently and accurately – even very small claims, which often don’t even get filed.

These advanced data collection and analysis systems can be applied to any kind of insurance product. Property insurers, for example (with the consent of customers) could utilize data collected by smart home devices to analyze the way a property is used; customers who set off smoke alarms more often, for example, might need to pay more for fire insurance, while customers who use energy efficient appliances with modern safety features could qualify for discounts. Although the relevant data is collected by devices and sensors, it goes largely unused. By developing a structure for it and including it in a database for AI-based analysis, that data could help companies and customers save money and get better coverage.

The same applies to vehicle insurance. Data collected by the braking, acceleration, fuel, and of course safety systems could help companies set optimal rates for customers, with a much wider variety of discounts available based on safe driving habits – for example, offering discounts to drivers who don’t travel at night, when the accident rate shoots up.

In another example, data on vehicles recorded by cameras in garages and outdoor parking areas – generally used for security, and not recorded in databases, could be used by insurance companies as a reference for vehicle damage. Customers who consent to having their vehicles added to the database could find their claims processed much faster; If a vehicle is listed as “healthy” in the database, any damage after a claim would clearly be due to the reported incident, and there would be no need to investigate whether the damage preceded the incident.

With advanced analysis fueled by the extended databases resulting from the collection and labeling of currently unstructured and hidden data, companies will also be able to process claims much more quickly – and accurately, thanks to the far greater level of detail they can glean. Companies will thus be able to perform online adjustments, eliminating the need for an adjuster to physically show up in order to inspect damage.

By eliminating that requirement, companies will be able to significantly reduce the deductibles customers need to meet for a claim, since they will have a much more accurate picture of what that claim is worth. This will open the door to enabling customers to file claims on even small amounts of damage – and companies will be able to pay out these claims with the money they save on reducing or eliminating the involvement of agents, paperwork, adjusters, and investigators in claim disputes. Using the detailed data garnered from AI-based analysis using formerly unstructured data, companies will be able to make informed and accurate decisions on claims of all sizes.

And detailed AI-based data analysis will be able to significantly reduce processing time. Today, even the simplest claims take weeks, if not months to process, with insurance teams required to physically inspect claims. With the far greater amount of usable data available as a result of collection and classification of currently unstructured data, companies will have all the resources they need to make accurate and correct decisions on claims – without requiring the customer to wait months for their check.

That’s good for insurance companies, too, as they will be able to better ensure customer loyalty – reducing or even eliminating this wait, which is the biggest complaint customers have across all types of insurance, and thus mitigating the churn that sees companies lose as many as half their customers annually to competitors.

Experts agree: The more data, the greater the competitive advantage for businesses, and businesses that think outside the “data box” – utilizing every possible source for data – are likely to have the greatest advantages. For insurance companies, those advantages – in the form of data gathered from a wider variety of sources that is currently going largely unused – are available right now. By taking advantage of unstructured data now companies will be not only more successful, but ahead of the curve and better positioned for the future when working with this type of data will be essential.

Neil Alliston

Neil is the VP Product and General Manager, Europe, at Ravin AI, where he is responsible for all product development and design across multiple verticals, as well as maintaining and building customer relationships across Europe.


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