Even for simple cases, claims management can be a complex process. From workers’ compensation claims to business insurance claims, every claim needs to go through a life cycle of documentation, incident investigation, claims adjustment, and more.
Businesses that file a high number of insurance claims may benefit from using claims analytics to identify patterns, ensure regulatory compliance, and mitigate the risk of insurance fraud. And of course, insurance companies themselves can use claims analytics to refine and streamline the claims administration process.
Here’s what claims analytics is all about, and how you can combine data analytics and insurance automation to make sense of your insurance claims data.
What Is Claims Analytics?
Claims analytics, or claims analysis, refers to the use of data mining software to gain insights into insurance data in a way that wouldn’t be possible to do just by looking at individual claims. It’s primarily used in the healthcare and insurance industries, but it can also be used by businesses for safety and risk management.
Claims analytics solutions may use artificial intelligence, machine learning, predictive analytics, and other data science technologies to locate patterns in unstructured data and provide valuable insights into the claims life cycle.
Companies can use claims analytics to help with decision-making, identify fraudulent claims, and optimize the claims management process.
Benefits of Claims Analytics
Claims analytics can help with everything from ensuring the profitability of your business to streamlining the customer experience and improving customer satisfaction. Here are four ways claims analytics can help you gain deeper insights into your data.
Find Patterns
First, claims analytics solutions help you find patterns in your claims data that you might otherwise overlook. Analyze data based on factors like the claim location, time of year, and the demographics of the claimant. You might discover that certain types of claims are more common during certain peak periods or among certain age groups.
Claims analytics tools typically use reporting dashboards and visualizations to display patterns in a way that’s easy for users to understand.
Make Better Decisions
When you can predict the frequency of claims with more accuracy, you can make better decisions about budgeting, workplace safety, and risk management. You can also make a more informed decision about what types of business insurance coverage you need and how much staff to have dedicated to claims processing.
Identify Fraud
Insurance fraud can be more easily detected with data analysis. By looking for unusual patterns, you can spot red flags more quickly and fast-track legitimate claims. You can conduct an investigation into suspicious workers’ compensation claims, and be better able to identify cases of Family and Medical Leave Act (FMLA) abuse or fraud.
Streamline Workflows
Finally, you can use claims analytics to streamline your claims workflows by identifying redundancies, eliminating data silos, and automating repetitive, time-consuming tasks. Digitize key steps in the claims management process to reduce paperwork, facilitate communication between claimants and adjusters, and collect more useful data.
3 Key Uses for Claims Analytics
Claims analysis can have several different applications depending on whether you’re a claims administrator, an HR professional, or an insurance adjuster. Here are a few use cases for claims analytics that can have a major impact on your bottom line.
Subrogation
Subrogation refers to the process of an insurance company pursuing damages from a third-party who may have been responsible for the loss, rather than the claimant.
Subrogation opportunities can be easy to miss, but data analytics tools can look for key words in incident reports and other documents that may point toward third-party liability. Pursuing subrogation opportunities can enable the insurance provider to recoup costs, while helping to keep premiums low for the policyholder.
Settlement
Settlement is the process by which an insurance company agrees to make a payment and settle a claim. A high settlement ratio means the company is efficient at resolving claims and meeting their obligations to policyholders.
Claims analytics software can streamline the settlement process by determining fair payout amounts and automatically settling straightforward claims.
Benchmarking
Benchmarking involves using claims analytics to see how your performance compares with that of your competitors. You can use claims analytics to compare your data to the industry average, to specific competitors, or to internal benchmarks.
Benchmarking can help you identify gaps or bottlenecks in your workflows, and find ways to optimize your claims processing so as to meet industry standards.
How to Implement a Claims Analytics Strategy
The power of data mining and claims analytics can revolutionize your business’s claims management process. But simply performing a claims analysis won’t get you where you need to be. Follow these steps to create an effective claims analytics strategy.
1. Assess Current Processes
First, assess your current processes to determine which areas have the most room for improvement. Conduct a workflow analysis or create a process flowchart so you can visualize the steps in your claims management process and look for inefficiencies.
Once you have a clear idea of your starting point, you’ll be able to perform a claims analysis that can pinpoint the changes you need to make to your process.
2. Set KPIs
Research industry benchmarks or set your own key performance indicators (KPIs) so you can track the results of any changes over time. Your goal could be to shorten the claims life cycle, increase your settlement ratio, or improve customer satisfaction.
3. Pool Existing Data
Next, take stock of your existing datasets and ensure that they’re in good working order. Incompatible or inaccessible data silos can slow down the claims analytics process.
Although some software tools can process unstructured data — such as written records that haven’t been digitized — aim to standardize your data collection process and store claims data safely and securely.
4. Enact Software Solutions
Now, choose the right claims analytics software for your specific use case. For example, if your immediate focus is on workers’ compensation claims, look for a platform that can handle accommodations and return-to-work event tracking, as well as incident reporting and Occupational Safety and Health Administration (OSHA) compliance.
Pulpstream is a no-code claims management platform that can help you process any type of claim, from workers’ compensation to product liability and warehouse claims. Our intuitive interface helps you enhance the claims management process without having to write any code.
5. Review and Optimize
Finally, review the results of your claims analysis and continue to make use of claims analytics overtime. From streamlining workflows to optimizing insurance settlements, claims analytics can help you make data-driven decisions in real-time.
Streamline Claims Management With Pulpstream
Claims analytics involves using data mining and data analysis to discover trends in your claims data that wouldn’t be obvious on an individual level. Claims analytics tools may use predictive analytics or machine learning to identify data patterns and predict future outcomes. Performing a claims analysis can help you identify fraud, uncover areas of business risk, or simply optimize your workflows to improve their efficiency.
Pulpstream helps businesses unlock the power of data analytics with our cloud-based claims management platform. From general liability to workers’ compensation claims, use Pulpstream to document workplace incidents, facilitate communication between claimants and claims adjusters, and create detailed loss run reports.
Use our intuitive dashboard to sort insurance claims by type, month, age group, and more. Get a free demo to see how it works!
