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10:53 AM
Michael Chochrek
Michael Chochrek

The Claims Data Quality Advantage: 4 Steps to Improved Claims Outcomes

When claims data is accurate, claims professionals can make more informed decisions, enhance customer service due to shorter cycle times and a more proactive claims process, and ensure that metrics and performance benchmarks will be more precise.

All too often, business decisions in insurance claims departments are based on information generated from poor or missing data. This lack of quality data can be caused by a number of different factors, such as employee turnover, human error in the form of misspellings and omissions, and multiple claims systems that can corrupt data. Bad data prevents claims professionals from effectively managing key business processes including loss reserving, reducing allocated expenses, maximizing subrogation recovery and accelerating claims settlements. For many claims professionals, improving the quality of claims data can seem like a daunting task. Fortunately, there is a clearly-defined process that will help them accomplish their business and claims processing objectives while measuring progress along the way.

1. Uncover What You Don't Know About Your Data

Cleansing data is a good start, but is only part of the equation. It is just as important to identify why the data was incorrect in the first place. As you begin to determine the root cause of the impaired data, it may come as a surprise how data points within the claims system are being used and populated by the claims staff.

[For more on claims data see With Claims Data, Size Doesn't Matter.]

For example, an inexperienced person may fail to select the appropriate injury code from a look-up table, or use inconsistent abbreviations in their notes fields. As the amount of impaired structured and unstructured data grows within a claims system, a claims professional's ability to analyze the results and make proper and timely decisions diminishes. Digging deeper into why the data quality is so poor will present opportunities to ensure that the data is correct going forward.

2. Develop Action Plans to Fix Data at the Source

When digging through data and working to understand the cause of the problem, it is critical for claims professionals to create and implement action plans such as adjuster training, realigning systems, reviewing remediation lists, and deploying application fixes to address data quality issues. Simply fixing the data for the purpose of reporting is not enough. The sooner a claims professional implements controls to ensure data is correct from its point of entry, they will be in a position to better analyze their department's performance and results.

3. Distribute Information to Those Most Likely to Act on it Responsibly

Once the data has been cleansed and the source of the problem identified, information must be placed in the hands of those who can best act on it. For example, remediation lists of claims that should have been passed on to subrogation or those that were reserved inadequately, should be sent to the appropriate claims managers to oversee their resolution.

In some organizations, it is also important to ensure that claims information is passed on to other business groups to help them make informed decisions. Examples of this include the underwriting group being made aware of certain high-risk customers and the actuarial group having the financial data to properly run their models.

4. Continually Monitor Data Quality

When a thorough data quality process has been implemented, claims professionals must monitor the data to ensure continuous improvement. Any data defects need to be identified in a timely manner and with consistency so they can be addressed before affecting results downstream.

This can help claims professionals evaluate the performance of their group by looking at key indicators, such as current loss ratio, increase in severities, and expense trend lines. The best way to ensure data defects do not reenter a claims system is through a combination of periodic reviews of remediation lists coupled with measuring data quality metrics to verify that action plans are having a positive impact.

The Impact of Clean Claims Data

When claims data is accurate, claims professionals can make more informed decisions, enhance customer service due to shorter cycle times and a more proactive claims process, and ensure that metrics and performance benchmarks will be more precise. Insurance companies will also benefit from bottom line improvements due to an increase in recoveries and better management of allocated expenses, as well as revenue growth through the identification of new business opportunities. Whether in response to the tight margins across the insurance industry or stiffer competition, now is the time for a claims department to get their house in order and address the data quality issues they have been accepting for decades.

About the Author: Michael Chochrek is insurance solutions principal onsultant at Billerica, Mass.-based Harte-Hanks Trillium Software.

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