Use of data and analytics in the insurance industry has become a valuable tool for previously unseen claim insights, such as predicting the likelihood of a claim being filed, identifying potentially fraudulent activity, ensuring a positive customer experience through onboarding and beyond, as well as modeling, scoring and reconciling disparate data sets. Data is essential to the insurance industry, and regardless of its purpose, if the information is not accurate and relevant, its usage will have a negative impact across the organization.
Without high quality data, insurers cannot trust insights, reports, or even business decisions. As a result, it is crucial for insurers to ensure the integrity and suitability of data across the enterprise to generate critical business intelligence and establish a competitive advantage.
The insurance business is complicated.
Take, for example, a life and health insurance company that provides a wide range of individual and employer benefits. The company is required to create an annual census and onboarding process for disparate employees, dependents, types of coverage and benefit files. The first step of this process is to reconcile and validate all files for data integrity.
Often, this is an arduous, manual and multistep process to assure accurate information in the final benefits application. To increase efficiency, reduce errors and ensure high integrity data not only for benefits applications but also for new business insights, insurers need new processes and technologies to automate these time-consuming, manual tasks.
Data must be managed appropriately so that it remains reliable, relevant and accurate as it moves through the complex data supply chain. Maintaining high-quality data throughout its lifecycle is difficult. It requires a well-developed data management strategy with the appropriate policies and methods in place to ensure data is effectively leveraged for crucial insights.
To develop a deep understanding of data and to quantify its quality value, insurers must establish protocols for data management, data access, use and integrity. Critical to data management is a comprehensive data governance program that scores and monitors data quality and establishes data integrity controls throughout the data supply chain. The program must perform checks for reasonability, timeliness, balancing and reconciliation, and statistical controls.
Data governance also provides business users with easily accessible data and empowers them to analyze it to quickly solve business issues. A comprehensive data governance program assures that business users are not only aware of the quality of data, but also its lineage and ownership. That way, they’re choosing the right data assets for the right purpose, and the results are both trustworthy and meaningful.
Once a strategy is developed, insurers need a data intelligence platform that integrates extensive capabilities for data governance, data quality and analytics. Data governance capabilities provide transparency into an organization’s data landscape, including the data available, its location, assigned data owners/stewards and lineage to develop strategic insights.
By employing analytics that use machine learning algorithms, insurers can apply self-learning capabilities to continuously monitor and improve data integrity, encouraging data utilization and ensuring successful business decisions and insights. With well governed and quality data, insurers no longer struggle to incorporate high integrity data into benefits applications.
Let’s revisit our earlier example. A life and health insurance company used a manual process to reconcile and validate for data quality. The same company faced challenges streamlining annual census and onboarding processes for various employees, dependents, types of coverage and benefit files.
By establishing an all-inclusive data governance strategy and implementing a data intelligence platform, the same insurer was able to model, score and reconcile disparate data sets so all enrollees are on-boarded correctly. By automating the data quality process, the company no longer required hundreds of workers to perform manual data quality tasks, freeing up resources for other projects across the enterprise.
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