Big data is helping insurance companies crunch the vast amounts of data generated from their users. And when you can process large amounts of data, new insights get revealed—even on offerings companies have provided for years. These insights can provide powerful insights into pricing, risk, behaviour, and even fraud.
But exactly how does big data unveil these insights? And what does this mean for your company? Let's dive deeper into the best big data applications in insurance.
One of the big data applications in insurance is the ability to take and process telematics data from a car in real-time. The driver can use telematics data for various reasons. For instance, it could help the driver find the safest route on a case-by-case basis or help drivers adjust behaviour given certain road or traffic conditions, all in real-time. This would likewise work with autonomous vehicles.
For the insurer, the data allows for more accurate risk pricing. Insurers can gain insights into every single driver's journey, taking each as an isolated event. Rather than amounting every driver to one giant average, insurers can create precise risk pricing based on circumstance, meaning premiums can be adjusted by the second (rather than annually or even longer).
Understanding Customer Behaviour
Customer behaviour is one of the most prominent big data applications in insurance. Now, behaviour is measurable on a previously inconceivable level.
Consider driver behaviour. How fast and accurately do they corner? When do they speed? When are they distracted? Understanding the risk of your customer's actions is a powerful big data application in insurance, but it's certainly not the only one.
You can also measure your customer's behaviour in how much time they spend on a website, how long they leave a product in their cart, which emails they do and do not respond to, and which CTAs appeal to them. With these in mind, another big data application in insurance is that it can process customer behaviour over millions of customers and identify patterns and behaviour that might not have been seen before.
For instance, you might have the insight that customers who buy embedded insurance immediately get into fewer accidents than those who do it retroactively. Not only this, but you can use this strategy to acquire new customers and price new customers by their purchasing patterns.
Fraudulent claims can cause large amounts of loss to insurance companies, and it's a complex problem to fix. According to the National Insurance Crime Bureau (NICB), motor insurance fraud tacks on an extra $200-$300 to customers' insurance premiums annually.
Companies once had to constantly review and update their fraud detection schemes to minimize their losses. Now, however, fraud detection is an essential big data application in insurance. With big data, spotting fake accident claims is now easier than ever before.
With big data, you can analyze everything about accidents, including the car's speed and trajectory, the shape of the road, and where the vehicle stopped after the crash. Each can provide real insights and judge if a claim is viable.
This is a big data application in insurance that lowers the level of risk.
More Accurate Pricing
No matter the type of insurance you have, whether it's UBI, embedded, or short-term insurance, big data allows you to gain transformational insights into your pricing model. With more information, you can understand individual risk and determine more accurate pricing on a second-by-second level and holistically.
Doing so not only makes your pricing scheme more accurate, but it also allows you to better anticipate your financial projections. Finance and accounting teams inside insurance companies will have less guesswork with better predictive models, as well as pricing and risk that they can count on.
Big data applications in insurance are here to stay
The insurance industry is advancing rapidly, and big data is a huge part of it.
If you want to discuss how big data applications in insurance can help your company, you have come to the right place.