Underwriting insurance policies is all about understanding and mitigating risk. It is the job of the insurance underwriter to evaluate the risk of potential clients. In today’s interconnected world, underwriting insurance has changed. According to Business Insurance, “companies cannot approach risk the same way they have in the past, and the intelligent use of data can make the difference.”
Because of usage-based insurance, IoT, and all of the interconnected devices out there, insurers have more data to work with than ever before when it comes to underwriting insurance. Let’s look at the automobile insurance industry and how they typically underwrite insurance policies.
Underwriting Automobile Insurance
When underwriting automobile insurance, traditionally an insurer looks at the customer’s driving record and the make and model of the automobile, the latter being the most critical. This helps the insurer determine the cost of the customer’s policy and if they’re insurable. But with the advent of technology and the ability to monitor different types of data feeds, things have changed and insurers have much more internal and third party data to use outside of just a driving record and Vehicle Identification Number (VIN).
Today, we have connected vehicles and smartphone applications that can actually track driving habits and share them directly with an insurer. These devices can track how fast someone is accelerating, the sharpness of turns they are making, sudden stops and much more. This gives insurers a lot more data to work with when underwriting insurance policies. It also helps them create more personalized policies for individual customers.
To capitalize on non-traditional insurance, you have to leverage data from interconnected devices, which is largely based on the assumption you have good data quality and transparency for drawing conclusions before underwriting. So, the next step is ensuring you have quality data.
Ensuring Data Quality
To ensure data quality, insurers need a data integrity and analytics solution that automatically monitors an organization’s data flow as it comes in through interconnected devices and continuously moves throughout an enterprise. The solution conducts field level checks into the completeness, type conformance, value conformance, and consistency of data at its origination, identifying any potential data quality issues at the point of creation.
By analyzing data both at summary and transaction level as data is exchanged and transformed throughout the data supply chain, insurers can verify, balance, reconcile, and track all of the data being processed to ensure its accuracy. Once the data is accurate, insurers are able capitalize on their quality data to successfully underwrite insurance policies.
To learn more about data integrity, download the eBook below.
For a deeper dive into this topic, visit our resource center. Here you will find a broad selection of content that represents the compiled wisdom, experience, and advice of our seasoned data experts and thought leaders.