Like so many other industries, insurance is becoming increasingly data-driven. Data, of course, has always been an essential 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 objective decisions.

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

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. Wellness device apps record health, activity, exercise, and lifestyle data. Users voluntarily provide virtually all this data 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. Then, companies could analyze it with advanced AI- and machine learning-based systems to 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 outcomes possible. 

Advance Data Collection and Analysis

With advanced data collection and analysis, insurance companies can save money, eliminate inefficiencies, offer better and more relevant products, and ensure that they provide the right products to the right customers. In addition, 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. Utilizing unstructured data, companies will be able to develop as detailed insights 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 minimal claims, which often don’t even get filed. 

Advanced-Data Applied to Insurance Products

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. As a result, customers who set off smoke alarms more often, for example, might need to pay more for fire insurance. In contrast, customers who use energy-efficient appliances with modern safety features could qualify for discounts.

Although devices and sensors collect the relevant data, 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.

What Data Applies to Vehicle Insurance?

The same applies to vehicle insurance. But, again, 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 have their vehicles added to the database could process their claims faster. If a vehicle is listed as “healthy” in the database, any damage after a claim would clearly be due to the reported incident. There would be no need to investigate whether the damage preceded the incident.

Expediting Claims Faster

With advanced analysis fueled by the comprehensive 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 to inspect the damage. 

Reducing Customers Deductibles

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. In addition, 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.

Significantly Reducing Processing Time

And detailed AI-based data analysis will be able to reduce processing time significantly. Today, even the most straightforward 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 due to the 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. 

Ensuring Customer Loyalty

That’s good for insurance companies, too, as they will be able to ensure customer loyalty better – 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 most significant advantages.

For insurance companies, those advantages – in the form of data gathered from a wider variety of sources that are currently going largely unused  – are available right now.

By taking advantage of unstructured data now, companies will be more successful and ahead of the curve, and better positioned for the future when working with this type of data will be essential. 

Image Credit: by Mikhail Nilov; Pexels; Thank you!

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.