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Insurance Organizations Depend on the Quality of Their Data

Authors Photo Precisely Editor | February 23, 2024

Insurance is an inherently data-driven industry. Even before the age of advanced analytics, experts in the industry were routinely using data to assess risk and price policies. Today, data analytics plays a more important role than ever. Innovators are in a race to see who can use it to their best advantage.

Insurance carriers have far more powerful tools at their disposal than in the past, enabling them to more accurately profile their customers, evaluate risk, and drive new business. Their ability to generate business value is directly related to the quality of their data, however. Unless they have high-quality data, business users simply cannot deliver optimal results.

A 2023 survey conducted by Arizent and Digital Insurance on behalf of Precisely reveals a lot about the state of data-driven decisions in the industry. Of the senior insurance executives and managers who participated in the study, nearly half said that their organization’s data-driven digital transformation efforts match those of other carriers. Nearly a quarter rated their company’s performance as superior to that of other insurers.

What distinguishes that top quartile from the rest of the pack? The survey uncovered some interesting perspectives on how the top 24% of companies are accelerating digital transformation.

colleagues revising the data quality

How Industry Leaders Get Superior Results

The majority of respondents in the Arizent/Digital Insurance study rated their data management processes as being only moderately effective at meeting the core criteria for success. The top quartile, in contrast, reported better results, which were supported by better processes and technology.

Organizations at the leading edge of data management are more likely than other insurers to make certain that their data meets minimum requirements, applying business rules to affirm the accuracy of their data. These top-tier companies use validation and reconciliation tools to outperform the competition.

Nearly all the leading-edge firms said that they have controls in place to ensure that data entry meets clearly defined business rules. That practice is far less prevalent among companies in the lower three quartiles. Top performers are much more likely to use technology systems to validate addresses and other geographic data points. This is especially important when matching third-party data with internal data sets.

Companies that lack well-defined processes and supporting technology are dependent on internal staff to manage data quality as best they can. Yet respondents in the survey clearly understand this to be an inferior approach. Only 26% regard this tactic to be highly effective, whereas more than 40% indicate a strong preference for automated systems and scalable data validation tools.

Scalable Data Quality Systems Drive Profitability

These findings should not come as a surprise. Insurers that use technology to improve the accuracy of their data can mitigate risk, improve operational efficiencies, and achieve end-to-end financial reconciliation.

Increased data quality reduces opportunities for human error to enter into the picture and supports automated workflows governed by well-defined data quality rules. That, in turn, makes for more efficient operations, enabling staff to spend their valuable time on higher-value activities.

Perhaps one of the most important advantages of high data quality arises from the use of advanced analytics for risk assessment. Property and casualty insurers, for example, use advanced location intelligence to develop fine-grained models for wildfire risk. Using curated datasets that include information about insect and disease damage to vegetation, fire experts can gain a better understanding of conditions on the ground. Forests of so-called “red and dead” standing timber present a much higher risk of wildfire than healthy woodlands.

coworkers revising their high quality data

By analyzing the prevailing wind direction, the position of buildings and their construction materials, and proximity to combustible vegetation, insurers can gain a much better understanding of wildfire risk for specific properties.

Companies that write auto insurance policies, likewise, can use location intelligence to understand traffic patterns, crime rates, and even the exact location of the driveway where a policyholder routinely parks their car.

Data quality remains an issue for most insurance carriers.  Researchers for the Arizent/Digital Insurance survey found that the negative impact of poor data quality was widespread. In some cases, mismatched records led to incorrect mailings and poor communication with customers. For others, inaccurate data led to poor quality decisions, conflicting reports, mispriced policies, delays in claims processing, and other significant problems.

White Paper

The Quality of Your Business Depends on the Quality of Your Data

Read this white paper to explore how insurers can drive growth through accurate data validation and reconciliation.

Building Data Quality at Scale

Developing and maintaining data quality at scale requires a systematic approach, supported by the right enterprise-grade technology tools. Most enterprises, including insurance companies, struggle with the constantly changing landscape of systems and data sources throughout their organizations. The best data quality tools adapt easily as your company changes and grows.

Data quality is just one very important element of data integrity. Data integrity incorporates a broader spectrum of attributes, bringing together data integration, data governance, location intelligence, data enrichment, and data quality. It’s ultimately aimed at ensuring that data is accurate, consistent, and fully contextual. When data is well governed, it is available to the right people in the right place at the right time, and it’s compliant with applicable regulations, standards, and internal policies.

Precisely helps insurance carriers and other organizations achieve their strategic objectives and increase profitability by building and maintaining data integrity at scale. Our Data Integrity Suite is a modular, interoperable toolset that includes everything you need to deliver accurate, consistent, contextual data to your business – wherever and whenever it may be needed.

If you would like to explore how insurers can drive growth through accurate data validation and reconciliation, dig into the survey results from Arizent and Digital Insurance. Download your copy of the white paper today: The Quality of Your Business Depends on the Quality of Your Data.