As discussed in the previous section, renter’s insurance policies are primarily an amalgam of data points for assessing risk on the property, location, along with consideration of the renter’s history. It would ordinarily take an army of teams to determine risk offline to underwrite policies and process claims. However, the time required to process all these data points have come down considerably due to the availability of data from multiple online sources. However, not every insurer can work at the speed and level of personalization that entails analyzing all these data points at scale. Let us understand what effort it takes to underwrite and issue renter policies, and how they leave much room for improvement.
The key to understanding the points of origination of the renter’s journey is that it is a natural expression of their aspirations to move into a rented home. This means many digital insurance cases could be triggered at the point of closing or agreement, which is why established insurers and brokers encourage renters to apply for a policy at that point. However, the typical application process does not personalize the process enough to make the initial quote compelling enough. It also requires multiple back-and-forth conversations to create an attractive offer that captures all the requirements and price sensitivity for the renter.
Data Collection for KYC
The home automation business in the United States has practically doubled in the short span of two years. Neos has shown that they could assess IoT-enabled smart home automation to gauge and personalize insurance quotes accurately. However, not all insurers have the technical prowess to integrate with IoT-driven data sources, which leaves much room for personalization. If that were not enough, many of them are still very reliant on traditional methods of data collection and paperwork, which can tremendously undermine opportunities for pricing, and increase friction between the insurer and customer.
Risk Review and Scoring
Underwriting for renter’s insurance is complicated due to the multitude of data points involved and can take considerable time, but has traditionally relied on historical data to model risk scoring. But rapid and millennial aversion to home owning have forced insurers to consider changing their forecasting approaches to real-time modeling using machine learning. However, not all providers have been able to scale their risk scoring framework elegantly due to aging tech infrastructure and organizational silos.
Insurers typically issue policy paperwork in an issue-and-forget approach, where they seldom engage their customers after the policy is issued, except for reminders to pay the periodic premiums. The newer batch of insurtech startups has exploited this opportunity to create an engaged community of customers. They have been able to educate and upsell the potential advantages of mitigating the risk of their rental property valuables in volatile weather and urban conditions.
First Notice of Loss and Claims Processing
In case of property loss and casualty scenarios, renters are typically stretched between recovering from their stressful valuable or property loss, and providing the insurer appropriate documentation to process their claims. Such claims are usually a game of going in circles, with multiple points of evidence requested by the insurer, leading to an annoying front-line experience. In addition to that, renters are usually on their own when dealing with multiple related parties like service providers. The insurer is not able to provide a compelling claims experience since they are not integrated into this part of the ecosystem.
InsurTech providers have exploited these gaping inefficiencies in the renter’s journey to gain an edge over established insurers. They have also used technology-first innovation by bridging the gap between the multiple set of players in the property ecosystem.
But now insurers with trailing tech capabilities can level the playing field with end-to-end CJaaS capabilities. This is where CJaaS workflow automation players like Autonom8 bring value.