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How Can Big Data Be Used In Insurance Claims Analytics?

MD: Big data helps the analysis of large amounts of structured and unstructured data. Let us see how health care providers and insurers can use big data in insurance claims analytics.
Big data is the technology that facilitates the analysis of large amounts of structured and unstructured data. Healthcare providers and insurers can use big data to devise models. Machine learning algorithms can be developed to train insurance claims analytics machines to evolve as per changing dynamics of data. The use of big data can be used to simplify insurance claims processes and streamline workflows.

4 Ways How Big Data Can Help In Insurance Claims Analytics
Big data can be helpful in insurance claims analytics in several ways. Listed below are some of them.

1. Insurance claims analytics can use big data to speed up claims processing
Big data in insurance claims analytics has helped insurers find anomalies and inconsistencies in insurance claims data. Large amounts of data for insurance analytics help determine the accuracy of the finding instead of finding anomalies based on a limited data set.
Companies can use historical data and match claims against this data to derive unique analytical results. For example, one of the analytical data points from this activity is finding insurance adjusters to work on insurance claims. The credential and experience of the insurance adjuster often determine the time for the insurance claim to be resolved.

2. Reduce fraud
The FBI came up with a statistic that is revealing and horrifying. FBI estimates that health insurance payers spend an enormous amount annually on insurance fraud. This startling estimation suggests that existing insurance claims analytics solutions are getting it wrong, or the fraudsters are becoming smarter.
Many insurance claims analytics software vendors use big data in insurance claim analytics to identify fraud early. Complex and adaptive predictive analysis is used for this, and it helps detect even the most innocuous and trivial of manipulations. It is possible to identify suspicious activity and spot potential offenders.

3. Claims settlement
Insurance claims analytics in tandem with big data can expedite claims settlement. In the insurance process, claims settlement is time-consuming, and the adjuster requires doing a lot of manual work. The adjuster must rely on outdated ways to assess the loss and arrive at a claim settlement amount.
Big data-backed insurance claims or insurance data analytics can use historical data to analyze claims and suggest workflows to expedite the claim. Familiar claims can have a proven, tried, and tested workflow. Novel claims can have another automated workflow that reduces the manual workload on the adjuster and helps determine the claim's validity.

4. Gaining insights to provide customer service
Insurers can use data-driven insurance claims analytics solutions to generate patient preemptive care recommendations. Such solutions can be used by physicians, medical practitioners, and hospital departments, and insurance healthcare claims data can form prescriptive analytics. Patients can be advised on changes to their healthcare plans, lifestyle, diet, sleep, medication, and even healthcare practitioners. The insurance company can also suggest telemedicine appointments to patients who have been identified as having a higher risk of contracting medical conditions.

Conclusion
The use of big data will shape how the insurance industry operates in the coming century. The maturing of artificial intelligence and machine learning technologies will create a potent mix of technologies that insurance claims analytics solutions can leverage.
Learn more from:
https://www.osplabs.com/insurance-claim-analytics/