According to Angela Chan, Surefyre’s senior customer success manager, wholesale agents and MGAs are starting to bear witness to the benefits and efficiency of digitized insurance applications and better data analytics.
“Once you digitize an application, when you do supplemental data entry, all of that information is interconnected,” she said.
“This means it’s easy for you to pull that data, whether you can pull the reporting yourself or integrate with a third-party analytical system. You can screen through those 1,000s of data points that probably aren’t very useful.”
This is in contrast to more traditional ways of processing an application through paper records, which potentially cuts the amount of information available to the insurance supply chain.
“If you’re getting property data from a third party but it’s done through paper, there’s no intersection where you can group all that together and really run analytics on it to see what type of property is doing really well or what areas or regions are prone to claims,” Chan said.
“I don’t think agents had enough information or the right tools to make that easy for them previously.”
Additionally, having digital applications and a better grasp of data analytics can bring to light the agents that bring quality applications, higher ratios and shorter turnaround times.
“These agents can be staying with you year after year, but wholesalers and MGAs are probably wondering what the timeframe is between when you get them a quote and when they actually give you the binder,” Chan said.
“It can help really prioritize the underwriters’ workflow, but also from a wholesale and MGA standpoint, [it can help assess] which agents are really your high-level agents providing you with quality submissions for you to kind of target more.”
In an interview with Insurance Business, Chan outlined why predictive modeling can be difficult to achieve for insurance companies interested in its capabilities and why there is a lot of innovation from insurtechs catering to companies wanting stronger data analytics.
While mastering predictive modeling is on the agenda for many companies in the insurance supply chain, Chan believes that gathering and using the correct data is difficult right now.
“Everyone’s trying to be a leader in predictive modeling,” she said.
Certain carriers may use custom software, where they can input certain information that is associated with an account to help get an idea of what a loss ratio might be and how they should go about pricing.
“But that generally is stored outside of your application,” Chan said.
“It was very tough to, I think, aggregate all that data together in a centralized place for them to run reports or extract that data to provide to third parties.”
Now, there seems to be a bigger push to leverage technology to create a more accessible space for agents and MGAs to extract what they need to bolster their underwriting capabilities.
“I think there’s been so much data out there that no-one’s been able to use because it’s not easily accessible, and therefore, they just kind of don’t look at it,” Chan added.
Alongside an effort to consolidate predictive modeling, wholesale agents and smaller carriers are also pushing to have better data analytics to keep up with the big players with more resources at their disposal.
“With bigger carriers, it’s easier for them to kind of have a whole team to help you sort through that data,” Chan said.
Meanwhile, midsized and smaller companies have realized it is much harder to gather useful data when collecting information traditionally through email or PDF submissions.
“We’re seeing a big shift now in building customizable portals and web forms versus five or 10 years ago, when email was seen as sufficient,” Chan said.
With this shift towards bigger data across the industry, smaller businesses are increasingly looking to third party providers to build similar APIs that big carriers may have in-house.
“However, it’s very important for these third-party vendors to be able to connect with each other to pass data back and forth, so their customers can access this data a little bit easier,” Chan said.
Chan pointed to the opportunity that businesses can now take in finding specialized coverage and solutions in hard-to-place areas.
“If someone has a lot of data on California homeowners’ market - where are the fires happening and the likelihood this area will burn - and you’re willing to take a risk, you can become a big player in that market,” she said.
“No-one wants to go into their market space for that specialized line without a lot of empirical data to back up their findings. It was much more difficult 10 years ago to farm that data and deliver solutions to the people that desperately need it.”
