Filed Under:Technology, Analytics & Data

Dirty Data: Surmounting Insurance Data Quality Hurdles

 

Policyholder contact details exist in every insurance organization. This information is critical to operations and allows insurers to serve their policyholders appropriately. But the difficulty of maintaining this data is almost as prevalent as insurers having contact information in the first place. Because of the fluidity of relationships, contact data can be difficult to clean and keep up to date.

Most insurance organizations work to keep clean data, but there are many that view inaccurate data as a standard part of doing business. While that is true to a certain extent, data can be maintained and cleaned to enable more efficient and cost effective business practices.

To keep data clean and up to date, insurers should understand why contact data is important, common strategies for cleaning data, and how to overcome common objections to implementing a data quality strategy.

Where Is Contact Data Used?

Contact data touches nearly every part of an insurance organization. Each department relies on this information not only for communications, but also for a host of other business practices. Some examples of these departments are policy services, claims, underwriting, and finance.

Policy services and claims are obvious departments that use contact data, as they communicate with policyholders on a regular basis. They use contact information to send federally regulated notifications to customers regarding changes in their policies or other information. Claims departments specifically use contact data to determine the validity of a claim, and then to mail payments to individuals and businesses using those same contact details.

Underwriting uses contact data, particularly address data, to assess the risk on a given property and to set rates for coverage. If contact data is inaccurate, an insurer may take on an unknown risk or provide an incorrect rate quote for a new policy.

Finally, finance departments use contact data for billing and collections, but also for tax purposes. Each insurer is assessed taxes based on property location. If an address is incorrect in the database, then the insurer may not report the correct taxes back to each state.

It is important to realize how much contact data affects organizations, and therefore, to ensure its accuracy. While this seems like a simple piece of information to maintain, errors in collecting contact data frequently occur and negatively impact individual departments as well as the broader organization.

Developing a Strategy

Many organizations realize the benefit of maintaining accurate data, but they struggle to achieve it. According to a recent Experian QAS study, 87 percent of organizations manage the accuracy of their contact data in some way, but 92 percent do not completely trust their data in terms of it being completely clean, accurate, and up to date.

While there are processes in place to address this concern, organizations can improve those practices to institute a more integrated strategy. Data quality should be a standard part of daily operations instead of a simple “clean” that happens every quarter.

The first step in any data strategy development process is analyzing internal data to find out where errors occur and what types of mistakes are most prevalent. Once those are identified, an appropriate strategy can be determined.

Some common practices that can be implemented to cleanse contact data include:

Staff Training – Training is essential to any data strategy. Train staff members on the importance of data quality and let them know how inaccurate information affects the insurance organization.

Manual Cleansing – Reviewing information manually is common for many insurers. However, this process lends itself to human error. While a certain amount of manual checking and cleansing is important, insurers should limit manual processes, as they are often time-consuming and imprecise.

Duplicate Removal – Consolidating records can be done automatically with software or through a manual process. While software tools will be more accurate, they may not be cost-effective, depending on the size of the organization. Insurers should review the size of their database to determine which solution is best.

Point-of-Capture Validation – Verifying information as it is collected ensures its accuracy, eliminating the need to reach out to customers to correct misinformation. Point-of-capture validation corrects information as it is entered and prompts the user for missing details, like an apartment number. It cleans data before it enters underwriting and claims processes and allows for more efficient processing.

Any and all of these practices can be used in conjunction with one another to help form a data cleansing strategy. It is important to realize that there is no one-size-fits-all solution. Every business has its own data quality needs and data should be thoroughly reviewed before putting new practices in place.

Objection Handling

As with any new project, there are barriers to implementing a data quality strategy. The following tips can be helpful in overcoming common objections.

Budget – Financial constraints are always an issue, especially as the country continues to recover from the recession. Insurers can roll out new software one department at a time to ensure the expected return on investment and promised benefits.

Time and Internal Resources – Any project requires a certain level of time commitment for implementation. New strategies can be rolled out over time to distribute required spending and manual processes. Additionally, more data quality tools are being moved into the cloud. Software-as-a-service solutions can alleviate a lot of internal processes required for on-site implementation.

Senior Management Support – Management support for projects must be secured. Review data to find out common errors and then find out how those errors are affecting the insurance organization. Is inaccurate information delaying claims, hurting staff efficiency, or damaging risk assessment? By answering those questions, business stakeholders can not only find out what solutions are needed, but also build a business case for senior management.

Dirty data is a problem that every insurance organization faces and it affects a variety of departments. But data inaccuracies do not need to be part of the standard operating procedure. Clean data will affect the business as a whole and help improve the risk assessment, operational efficiency and ultimately, the policyholder experience.

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