Like so many other industries, insurance is increasingly data-driven. Data, of course, has always been an important resource for claims, risk and coverage decisions. But in a data-driven scenario, data becomes the primary focus of this decision-making process, with artificial intelligence, machine learning, and other advanced analytics technologies exploring and refining data, enabling companies to make data more profitable, efficient and objective decisions.
Data, especially data collected from sensors and other IoT devices that are multiplying by the day, as well as AI could do much more for insurance companies. According to experts, AI systems only use a small quantity all available data; up to 90% of the data to be collected is “lost”.
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 are connected – are growing in popularity; modern vehicles are essentially mobile computers, with a plethora of sensors gathering information about almost every aspect of the driving experience and environment; and wellness device apps log health, activity, exercise, and lifestyle data. Almost all of this data is provided voluntarily by users, as part of their user agreements – and much of it is not used, simply because it is unstructured.
But in fact, this data collected in 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 too many -paid, fraud and other issues that distort the cost. of insurance, providing them with information that will ensure businesses – and customers – get the best possible results.
With advanced data collection and analysis, insurance companies can save money, eliminate inefficiencies, deliver better and more relevant products, and ensure they are delivering the right products to the right customers. . This data can be used to define risks, determine premiums, develop products, triage complaints, prevent fraud, improve customer loyalty and decide which markets to target. By using unstructured data, businesses will be able to develop information that is as accurate as possible, far more accurate than is currently possible.
And it can also benefit customers. With better data collection and analysis, companies will be able to handle complaints much more efficiently and accurately, even very small complaints, which often aren’t even filed.
These advanced data collection and analysis systems can be applied to any type of insurance product. Property insurers, for example (with customer consent) could use data collected by smart home devices to analyze how a property is being used; customers who set off smoke alarms more often, for example, might have to pay more for fire insurance, while customers who use energy-efficient appliances with modern safety features might get discounts. Although relevant data is collected by devices and sensors, it remains largely unused. By developing a structure for this and including it in a database for AI-based analysis, this data could help businesses and customers save money and get better coverage.
The same goes for car insurance. Data collected by braking, acceleration, fuel and of course safety systems could help companies set optimal rates for customers, with a wider variety of discounts available based on safe driving habits – for example, offering discounts to drivers who do not travel at night, when the the accident rate is soaring.
In another example, vehicle data recorded by cameras in garages and outdoor parking areas – usually used for security, and not recorded in databases, could be used by insurance companies as a reference. for vehicle damage. Customers who consent to their vehicles being added to the database could have their complaints 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 analytics powered by the extensive databases resulting from the collection and labeling of currently unstructured and hidden data, businesses will also be able to process claims much faster – and accurately, thanks to the level much higher detail than they can glean. Companies will be able to make adjustments online, eliminating the need for an expert to physically show up to inspect the damage.
By eliminating this requirement, companies will be able to significantly reduce the deductibles customers must pay for a claim, as they will have a much more accurate picture of the value of that claim. This will open the door to the ability for customers to file claims for even small amounts of damage – and businesses will be able to pay those claims with the money they save by reducing or eliminating the involvement of agents, paperwork, adjusters and investigators. disputes. Using the detailed data gathered from AI-powered analytics using previously unstructured data, businesses will be able to make informed and accurate decisions on claims of all sizes.
And detailed AI-based data analysis will significantly reduce processing time. Today, even the simplest complaints take weeks or even months to be processed, with insurance teams required to physically inspect claims. With the vastly greater amount of usable data available through the collection and classification of currently unstructured data, businesses will have all the resources they need to make accurate and correct claims decisions – without obliging the customer to wait months for its verification.
It is also a good thing for insurance companies, because they will be able to better retain their customers, by reducing or even eliminating this expectation, which is the biggest complaint customers have in all types of insurance, and thus mitigate the churn that causes businesses to lose up to half their customers each year to competitors.
Experts agree: The more data, the greater the competitive advantage for companies, and companies that step out of the “data box” – using all possible data sources – are likely to have the greatest advantages . For insurance companies, these benefits – in the form of data gathered from a wider variety of sources that are currently largely unused – are available now. By leveraging unstructured data now, businesses will not only be more successful, but they will be ahead of the curve and better positioned for the future when working with this type of data is critical.