Big data and AI recommend the next show you should watch on Netflix, and now they are simplifying the way you buy home insurance. Bill Martin, president and CEO of Plymouth Rock Home Assurance, discusses different ways insurance providers have leveraged technology to provide customers with a better home insurance experience.
Like agile systems development, the terms “big data†and “artificial intelligence†have taken such broad meaning that they have lost their meaning. Maybe that is not fair. Perhaps they are just easier ways to say that insurance companies are using much more data than they used to and trying many more modeling techniques to estimate future outcomes.Â
The fear about using terabytes of data with massive MIPS of computing power – the fear of mining seemingly private information with nefarious robots to falsely accuse you of being a bad risk fomenting the biggest black swan financial failure of all time – arises directly from this use of vague terms.Â
At the end of the day, the most successful insurance carriers do what makes common sense — analyze data to divide good risks from the bad. The amount of money spent on fit and small differences in segmentation ignore the truth insurance buyers have been telling us for a long time — who you buy from and how we treat the policyholder matters more than $5 differences in premium. Â
How Is AI Simplifying Home Insurance?
Imagine if the massive power of detailed data with machine learning was used not on pricing and underwriting but on improving the customer experience?
Big data algorithms and artificial intelligence, like predictive modeling, recommend the next show you should watch on Netflix based on your viewing preferences. They provide you with the quickest route to get through rush-hour traffic, and they even help police officers determine where and when a crime is likely to occur next.
The amount of data we use to model such behaviors is doubling annually. Paired with predictive modeling, defined as using statistics to predict outcomes, these innovative tools have provided businesses and consumers alike with helpful insights and more efficient processes. Now, these technology solutions are simplifying the way you buy home insurance.
Using Data To Empower the Empowered Consumer
The insurance industry has long had a reputation for being an innovation and technology laggard. The process of buying home insurance has been a poster child for how ignoring the opportunity technology gives us hurts our customers. In the name of accurate underwriting, precise price segmentation, and fraud prevention, our industry has required customers to answer dozens of questions that few homeowners knew offhand – like the age of their roof and the percentage of carpeting in their home.Â
And because the data collection oftentimes relied heavily on memory, guesswork, and good faith efforts, homeowner “guesstimates†often provided inaccurate answers that led to inaccurate home insurance quotes and less protection they needed.Â
So, we insurers responded with extensive validation of questions, further application “warrants†that void or reduce coverage if answered in error, and a situation where the first quote provided is never the price ultimately charged. Potential customers avoided shopping insurance as diligently as they avoid getting COVID.
But what if we use data to empower the empowered consumer? What if we caught up to products customers’ expectations to be able to do anything online or from their mobile phone with the touch of a button? How much more would customers pay for a more positive experience? Ask tech’s most successful companies, from Google to Apple, and you will have your answer. Â
Home insurance providers who take advantage of big data and predictive modeling will ultimately provide customers with more accurate policy quotes and superior customer service, all while mitigating fraud. Providers have leveraged these tools in the ways below to better the customer experience.
Purchasing a Policy
Using state and federal census information, Google Maps, troves of public data, and even local assessor databases, you can “break†the underwriting process. Enter an address, and a company can provide a rate they are willing to bind without changing. The search cost – or the opportunity cost of the time and energy spent searching for the best personal lines insurance – trend lower for the consumer, and the cost to provide the quote follows.
Improving Customer Service Calls
The same data and modeling can resolve customer service problems, monitor their response, and react to customer needs in real-time. Advanced data processing and analytics can detect and estimate customer satisfaction from data produced during a telephone conversation. Suppose a customer were to contact their insurance company to ask a question or report a claim. In that case, big data can help a customer service representative better understand why they are calling before the customer even speaks.Â
For example, if a customer calls from an area recently hit by a tornado, data from the National Weather Service can alert the representative that this call may be linked to the weather emergency. Based on this information, they can immediately escalate the call to a supervisor, if needed, with the necessary knowledge and authority to resolve the problem.
Detecting Fraud
False, inflated, or misrepresented claims are paid for – subsidized – by honest policyholders. Schemes take many forms, whether it’s a dishonest homeowner intentionally damaging their own house, contractors who inflate the cost of repairs, or a homeowner reporting stolen items that didn’t exist in the first place.Â
According to the FBIOpens a new window , annual estimates of the cost of insurance fraud in the U.S., excluding health insurance, are as high as $40 billion. There is less of a risk of missing “red flag†claims that don’t match up for insurance providers who leverage big data to mine public databases for property information. For example, if a homeowner claims a windstorm destroyed their backyard shed, detailed weather data can determine whether or not a windstorm hit the area, and aerial imagery can confirm if the structure existed.
Our industry has focused on the benefits of big data and predictive modeling on ourselves – our margins and our price competitiveness. It takes the courage to “let go†of measurably effective processes and product designs that trade a good customer experience for a less competitive rate. The least we can do is beat Netflix on the sophistication we use to bring joy to our customers.
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