Three ways big data is improving today’s insurance claims process

Embracing big data can cut costs and streamline operations 

Three ways big data is improving today's insurance claims process

Jul 27, 2021 / Data & Insights

Big data, defined as large volumes of unrefined data made of up complex statistics, is an increasingly hot topic for businesses across industries, and it is proving to be beneficial in the insurance ecosystem in several ways. According to Yes Magazine, implementing big data has led to 30% better access to insurance services, 40-70% savings, and 60% higher fraud detection rates for insurers and stakeholders. The power of data has been leveraged in recent years to simplify processes, especially when used in combination with technology.

Let’s cover the three most impactful ways big data can improve claims processing.

Speed up the claims process with comparisons to compare past claims or similar situations

While claims are unique by timeline, type of coverage, and incident, big data’s large volume of specifics can help to find consistencies across similar claim types. By applying machine learning algorithms to outcomes, companies can review historical cases and similar claims as well as examine the results to better match claims to adjusters based on experience and expertise.

Occasionally, comparison of claims could even lead to automatic settlement measures for certain cases. It often takes a great deal of time to analyze and accurately assess the needed payout on a claim. Accurately computing the loss reserve based on keywords from similar claims and other data analysis techniques can cut out excess time and effort to break down the details of the claim for payout, therefore speeding up the process from file to payment.

Reduce fraud through streamlined identification techniques

The FBI estimates that insurance fraud (not including healthcare) costs upwards of $40 million a year. Leveraging data can identify potential "red flag" trends across adjusters' reports, such as witness repeats or lawyers listed on multiple claims and can cut these costs for the end-insured plus keep premiums affordable, which reduces churn.

While criminals will no doubt continue to find ways to manipulate the claims processes, using algorithms and predictive analysis can more effectively help identify potential parties that are contributing to fraud at each step of the claims cycle. This ability to identify possible criminal activity early and often can help stop repeat offenders from committing fraud.

Cut costs across the ecosystem

Big data can cut costs by scanning text and images at the beginning of the claims process to differentiate between simple and complex claims. This ability to immediately identify more complex claims helps processors more efficiently allocate their time and resources across their workweek. In addition, using big data analytics tools for the straightforward claims on the agenda can speed up the process, leading to more satisfied clients.

Putting it all together

Data science is not a new concept in the insurance industry, but “big” data opens a whole new world of analysis and value-add. However, collecting data for data’s sake does not provide much value. Understanding the large volumes of data, identifying what is helpful, coming up with a plan for its intended applications, and then using it in a responsible way is what makes big data valuable in today’s data-rich and highly connected environment. Cutting costs and identifying potential fraud is just one slice of how big data can help insurance meet the needs and challenges of an increasingly digital world, and finding the right tools to handle that data is crucial for both carriers and agencies.