Data Quality

3 Ways InsurTechs can use data to conquer the market in 2022

Did you know that 80% of insurers are using predictive modeling to fight fraud?1 Predictive modeling, artificial intelligence and machine learning are changing the game for insurers who are battling with an increase of fraudulent claims. That’s just one of the many ways technology, fueled by powerful datasets, is changing the insurance industry. Our expert, Carson Collins, examines the ways the insurance industry is poised to evolve in 2022.

Meet the expert
Carson Collins
Carson Collins
Account Manager

Carson Collins has spent the last 5 years building his career in data licensing with Data Axle. Carson understands his client's challenges and is passionate about customizing solutions to best fit their needs and helping them exceed their business goals.


We’re only a few weeks into 2022, but tell us why you think InsurTech is one of the most buzzed about industries of the year?

The InsurTech space is one of the most heavily watched and invested in emerging markets in the world economy.2 Like any industry, the companies that thrive will be those that are constantly innovating and leveraging all the cutting-edge technologies at their disposal. There are many ways InsurTech companies are changing the face of the insurance industry. The insurance industry is a legacy industry – companies have been around for decades and have a reputation for being laggards with respect to technology. But there’s a huge opportunity for them thanks to the InsurTech industry, from better fraud detection to faster underwriting processing, streamlined operations and digitalized marketing efforts. In fact, the 2021 State of Insurance Fraud Technology Study found that insurers are using predictive modeling 25% more than they did in 2018 and text mining use has increased from 33% to 65% in three years.3


What emerging aspect of the InsurTech space do you believe will make waves in 2022?

One corner of the InsurTech industry that is primed to catch attention and success by the emergence of innovative technologies is property intelligence. Property intelligence companies cemented their value off-the-bat by supplying clear and measurable pre and post-event imagery of what is going on outside of a property to help speed up the assessment and claims space, reducing the need for timely and costly field visits. These solutions are now layering on added details about the inside of the property to get a more complete analysis of a location as well as gathering more predictive insights for the future.

Data used to understand the outside risks associated with a property include aerial imagery via drones as well as satellite radar and computer vision to collect and assess outside, physical property details such as roof conditions and neighboring hazards. Data used to understand the inside risks of a property include tenant occupancy data such as number of tenants, type of tenants by line of business, number of employees for each tenant, and even the use of IoT (Internet of Things) Edge devices located within businesses to collect and monitor data in real-time about a property.4


As the InsurTech space grows and becomes more competitive, how can these companies differentiate themselves?

Clean, accurate data – and a lot of it – is going to be the key differential for InsurTech companies. As COVID-19 continues to reshape the physical business landscape, an accurate ground-truth understanding of which businesses are open and operating and the specific office locations/addresses they are operating at is more elusive than ever. As a result, it can be exceedingly difficult for commercial lines focused InsurTech companies to accurately identify key risk factors associated with a particular business or commercial property.


To continue that line of thought - when it comes to data sources, what are the main criteria InsurTech companies should look for? 

Although InsurTech companies and their specific products/solutions come in many flavors, the key criteria for selecting a data source are consistent – regardless of specific use case. InsurTech companies and developers should consider the following components when looking for a data source to power their solutions:

1. Data Coverage

Data coverage is a difficult concept to do a needs analysis for as it varies widely from industry-to-industry and use-case-to-use-case. For InsurTech companies focused on commercial insurance coverage, it is about ensuring its data sources are aware of every active, operating business – regardless of the type of business, the size of business, or the geographical location of the business. The only way to achieve this is by working with a data provider that has a large and diverse set of sources to draw from because there is no single registry to lean on. Data providers that understand this are collecting business information from both offline and online sources, from broad-based as well as vertical-specific sources, from both 1st party and 3rd party sources, and more. By leveraging multiple and distinct types of sources, a data provider can ensure they don’t experience coverage gaps.

For example, coverage for Data Axle is not just the brick-and-mortar location on Main Street – but also the business operating out of the home, or those businesses operating inside other businesses, or even those in co-working spaces. Coverage is not just large or urban-based companies but also small owner-operated and rural-based companies. Without all businesses represented, true data insights are difficult, if not impossible, to come by. In addition to collecting details on operating businesses, Data Axle also captures location data pre-verification and creates a separate database to store this information. Having coverage of these nascent businesses is critical as obtaining insurance is one of the first things a business will do before or prior to opening their doors.

2. Data Accuracy

This is where things get harder in identifying the right data partner. The best data providers are those that have processes in place to balance goals of data coverage with data accuracy. Just as lack of coverage obscures true insights, inaccurate data does the same. The cost of inaccurate data and more generally, deficient quality, has been well documented over the years.5 Specific to the insurance industry, inaccurate or outdated information on either a customer or prospect can lead to fraud, missed marketing opportunities, inaccurately priced or suggested policies and added time and cost for insurance companies or their customers.

When you consider the sheer size of the insurance market, these costs are amplified, for example, the U.S. and Canadian insurance industry net premiums written totaled $1.9 trillion in 2020.6 Understanding the importance of data quality, and choosing a data partner that has exhaustive processes to verify the information they collect is critical. Even the most authoritative sources of business data have their challenges. For example, the Secretary of State’s new business registrations are a reliable source to identify a new business formation. However, just because a business has registered with the government doesn’t necessarily mean it has opened its doors yet. For this reason, Data Axle uses its verification processes to distinguish between businesses that are open and operating vs. those that are not yet operational.

3. Simplicity of Integration

The simple and accurate integration of third-party data into InsurTech and insurer systems is critical for improving data quality and accuracy rather than compounding inaccuracies. A data provider should offer comprehensive customer service, technical support, and proper documentation to seamlessly onboard new data and ensure smooth product operation. Data Axle has an extensive cleansing, standardization and management process to enforce highly standardized and accurate data. This data is available in flexible file formats and delivery methods including a suite of real-time APIs for matching, insights and searching.

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