Insurtech is the future, but for it to succeed, the data must be iterated consistently. Even when compared to other consumer financial services, auto insurance is a one-of-a-kind offering. To begin, a car insurance plan, at its foundation, is all about controlling the value of a car owner’s valued possession, the car. 

Numerous things contribute to this. Issuing a policy necessitates evaluating complex data about an individual, like marital status, driving history, credit history, motor vehicle data, and occasionally invasive details about your personal life. Car insurance is a financial product that requires a high level of individual information, so you can’t just buy it off the shelf or expect it to arrive in two days.

After years of data collecting investment, most of this information can now be accessed electronically and in near real-time, altering the consumer sales experience. However, this field is evolving, and gaining quick access to increasingly detailed medical records remains difficult. 

Another problem is turning this data into actionable insights. Many car insurance companies look to use algorithmic and automated methods for analysis and assessment to return a judgment to the applicant in seconds. These strategies can be challenging to interpret and explain.

As you may expect, this is much easier said than done. 

It took years to reach this level of maturity, and some car insurance companies still lack the digital infrastructure needed to capitalize on new data and processes. The insurance technology (insurtech) business is at a crossroads, mainly because of the rapid alterations brought about by the Covid-19 pandemic.

Insurtech data is a double-edged sword

For car insurance companies wanting to assess risk, electronic car insurance data gives a lot of information. However, due to a lack of uniformity in the auto insurance business, it can be complicated to consolidate it to examine it methodically. Different coding methods are used by providers for diagnosis and procedures, while some doctors prefer unstructured notes entirely.

Furthermore, access to the data remains challenging. Most car insurance data exchanges are regional, while some suppliers are attempting to give national coverage. Furthermore, while applicants grant access to these types of data sources when applying for an insurance policy, not all auto insurance companies accept a standardized authorization, resulting in extra paperwork and delays.

Despite these obstacles, the possibility of standardizing and integrating this data into existing streams remains compelling.

Iteration is the key to success

Many auto insurance companies have started new data implementation projects with historical retrospective analyses, which can be time-consuming and costly. To summarize, these types of data initiatives necessitate a tremendous degree of patience. Many car insurance companies, aided by the pandemic, are instead aiming to learn as they go by piloting data in tiny ways and establishing systems around it. This can necessitate a significant investment in human review at the outset. It is not always possible to learn as you go if the absorption of new data changes your assessment outcomes. 

Finally, deciding whether to undertake retrospective analysis or to try to learn as you go is a delicate balancing act.

All of this iteration, as awful as it may sound, is unavoidable. 

Telematics has evolved as a sophisticated, data-driven approach to accident risk in the car insurance market. The first car insurance companies to deploy the technology began gathering fundamental data points, such as speed, via devices fitted in cars. Data collecting evolved to incorporate acceleration and braking, GPS positioning, and road condition data. 

Analytics approaches expanded in tandem with the data, and today, telematics can provide a robust and personalized assessment of driver risk.

New data opens up new possibilities, even if it takes time to learn and react. Electronic car insurance data has the potential to significantly alter the purchasing experience for both good and bad drivers, who can expect more real-time offers and better pricing. This data, when combined with ongoing learning and improvement, has the potential to be genuinely transformative. 

Small datasets can be incorporated and tested in more detailed reviews to make the most accessible applications even more straightforward and then continue to build up from there, slowly allowing life insurance pricing — and the algorithms — to get even better for consumers. We’ll get there, slowly but surely.

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