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In a client-centric sector such as the insurance industry, data accuracy is incredibly important.
Data consumed by the insurance sector in the US alone is phenomenal. In fact, more than 5,965 separate insurance bodies countrywide and premiums totaled more than $1.3 trillion in 2019. Therefore, insurance data accuracy is growing more and more essential.
However, integrity and data quality issues with insurance data capture are growing. Due to ‘dirty data’ problems in the late 00s, the US mortgage industry lost a total of $360 billion. Fraud, too, is costing the industry $30 billion annually.
Therefore, a refreshed focus on accuracy and integrity in insurance data is in high demand. But how important is data accuracy in insurance? What are the direct knock-on effects of poor data quality? How can insurance companies fight against the rise of data laking?
Related Reading: 2021 Data Accuracy Guide
Why is Data Accuracy So Important in Insurance?
The insurance industry is one of many worldwide which provides an essential service. Millions of people rely on their insurance carriers to help them bounce back from unforeseen problems and calamities.
For example, in the US, health insurance is a must. Treatments, diagnoses, and necessary drug prescriptions are all chargeable. Therefore, households depend on reliable, accurate insurance policies to fund their healthcare.
Here are a few further reasons why data integrity in the insurance sphere is so essential.
Privacy and Data Accountability
In the age of data protection, insurance companies have duties of care to protect sensitive information. Across Europe, for example, this right is protected by the General Data Protection Regulation, or GDPR.
According to KPMG, the implications of low data privacy and accountability may result in reputational damage. What’s more, failure to protect insurance data adequately may also lead to private suits and claims.
Therefore, insurance companies need to be sure their data is accurate, protected, and organized. The threat of malicious attacks and data theft revolves around a lack of accuracy and clarity.
Related Reading: Data Quality Issues In Insurance
Better Customer Care
Ultimately, more accurate data and clearer correlations lead to better customer service. Premiums can be complicated, especially for the end-user or policyholder. Therefore, insurance companies have a collective duty, again, to ensure that their numbers are accurate.
Problems that can cause headaches for insurance experts and policyholders include duplicate data. Failure to compile data in a specific place and into one singular, accurate record could lead to mispricing. Outdated records, too, could lead to inaccurate policies, quotes, and extra expenditure for customers.
The more accurate insurance data is, the more specific policies and pricing will be. What’s more, data visualization for insurance agents becomes easier. This means policies will, in theory, become more cost-effective in the long run. It also will not hurt a company’s reputation.
Related Reading: How to Identify Missing Data & Ensure Completeness
Fraud Prevention
As mentioned, fraud costs the insurance industry billions of dollars each year. Insurance fraud arises in many different forms. However, carriers can help fight back against the rise of policy crimes by ensuring their records are up to date. It may be a case of ensuring their data is clear in its organization and its references.
According to the Insurance Information Institute, the US auto industry saved $128 million through detailed data checks. Such results arose as a result of refining the photo inspection process. Data such as photo ID and basic cleansing could help to slice costs for carriers across the country. Insurance data accuracy gives confidence back to agents fighting fraud.
Efficiency and Processing
Ultimately, inaccurate records and poor data collation can lead to more time and effort spent putting together policies.
Policyholders require quick action when they need to make claims. If insurance carrier data is poorly organized or is unclear in any way, time piles up. Again, this could impact a company’s reputation as well as the customer experience.
It could impact the quality of the end product for the policyholder. Inaccurate data may result in a policy that fails to protect a user in the way they expect. Crucially, the insurance industry revolves around data. That is why it is vital to make sure the data they work with is clean and easy to source, data hygiene is of upmost importance.
How Do Insurance Companies Collect Data?
Generally, insurance companies use a variety of methods to collect data, these include manual underwriting and data collections from multiple sources. Manual underwriting means taking time to pool together background data from a variety of sources. To manage risk and make predictions, underwriters need to know as much as possible about their customers.
Predictive analysis is not easy to set up. However, current technology, and emerging technology, are helping the industry find new ways to build profiles.
For example, carriers may start collecting data through customer targeting. They can set up their targeting profiles to therefore find policyholders that fit their briefs. Such an option is entirely possible through tailored web advertising, social media, and more.
Insurance carriers may also have access to a variety of public records. Policyholders and potential customers must understand that this data is necessary to build appropriate risk analysis. That, ultimately, is what helps companies to set rates and to offer adequate coverage.
In modern times, as a result of fraud, insurance bodies can no longer rely on customer input alone. They must work with multiple sources and data collections. With a transparent system to collect and clean this data in place, the future looks bright for the industry.
The Future of Data in Insurance
The insurance industry will always revolve around data. Without it, companies could lose billions of dollars, and policyholders could receive inadequate coverage for their needs. The importance of clean data is obvious to all in the industry.
Data insurance trends show that clean data collation (from multiple sources) could speed up claims. More importantly, clearer data automatically pulling from numerous banks and resources is likely to be more accurate. Manual underwriting could be making the insurance process more tedious for holders and agents alike.
Data insurance companies are using technology such as WinPure to help analyze their records with more apparent oversight. Insurance data accuracy may improve further in years to come, thanks to the rise of device-to-device communication. It is an exciting time for the data industry and with new trends emerging, it’s never been clearer the importance of data accuracy.
Right now, however, is the time for carriers to clean up their acts and their data.
