James and his friends at college love traveling. They travel at least once a year, and they love fast cars. Predictive analysis can tell us they would be interested in car insurance of about $700 a year and that they respond better to instant messaging. If you have never heard of James before, this process can seem like magic. However, predictive analysis is what is helping to make this magic happen and is providing a competitive edge to insurance companies. It is fast-tracking processes right through the value chain of marketing, underwriting, pricing, claims and everything else in between.
Data has Driven Insurance but It’s a Different Road Now
Insurance professionals are used to collecting data and handing it over to actuaries and underwriters. Actuaries are the ones who set the prices for insurance policies based on historical statistical methods that are often proprietary to an insurer. The problem with this is that data ends up sitting in silos and cannot be shared with agents and others in the system. This approach is no longer scalable in the competitive world of insurance.
Predictive analysis has come in as the next generation advancement to the historical approach and makes use of modern tools like Big Data and Artificial Intelligence. Both large and small insurance companies, along with many brokers, are upgrading their systems — so they can take advantage of Data Analytics and use it to its fullest potential. It is no surprise that industry insiders, who are already using this technology, say that it’s a brave new world ahead. Here are three areas where predictive analytics is benefitting car insurance companies.
1. Optimizing Insurance Pricing
Cars on the road are going to be at risk for minor, or even major, mishaps. Insurance companies generally pay out insurance claims to about 8% of their customers on an annual basis. This can range from a few hundred to thousands of dollars. The big insurance claims usually only comprise about 1% of the total claims, but can mean a pay-out of $10,000 or more. This is where machine learning can come in to predict whether a driver could be at risk of causing a large loss. Predictive analysis can do this at an accuracy of 78% and above, which is big news for insurers. Dynamic models of pricing can now be built for every customer while still remaining reasonable for the market.
This provides an advantage to insurance companies to come up with real-time pricing at the point of sale. By optimizing pricing and even creating new insurance services, the policyholder who is a good driver need not be lumped with the bad drivers and have to pay the same premium. It is a win-win situation.
2. Reducing Fraudulent Claims
Here are some surprising statistics when it comes to fraud in car insurance claims
- Insurers in US and Canada say that 5-10% of their claim costs are from fraud accounts
- Collectively, the Coalition Against Insurance Fraud estimates that $80 billion is siphoned off through such false claims
Now, predictive modeling is coming up with answers to reduce this problem by predicting who is more likely to commit fraud even before they file a claim. AI also keeps a watch on real time data and can alert the insurer evaluating the claim. For example, let’s say Marie X has put in a claim and yet her social media pages don’t show any indication of such an accident. If her posts are all about her trip skiing or hanging out with friends, then there is some discrepancy and her case merits a closer look.
AI can create fraudulent risk scores and remove any anomalies that can arise from human error when evaluating fraud claims. Algorithms can much more accurately detect fraudulent patterns and are helping companies to minimize the risk.
3. Improving Marketing by Forecasting Buyer Behavior
Traditional car insurance marketing used to design services using very broad parameters such as ‘24-30 years old, white, working, male with a medium to high income.’ Predictive marketers have tossed this aside and are seeing revenue growth that is higher than the industry average. They are using tools with the goal of retaining customers. After all, it takes a policy a few years to become profitable. Data is being used to identify new markets to target and forecast customer behavior. It can predict customers that are likely to leave (so that less time is spent on them) and to anticipate how different customers could react to discounts or bundled offerings.
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