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Four Areas Data Analytics Help Tackle Insurance Claims Processing Issues

Pamela Morgan, Insurance CIO Outlook | Wednesday, January 27, 2021

Instead of recognizing the scam at the later stages of processing, using AI data management can develop a chatbot during the First Notice of Loss (FNOL) stage and converse with the customer to record their responses.

Fremont, CA: It is hard for adjusters to sift through large quantities of insurance claims. It becomes challenging to have to deal with several policies and claims. Besides, they cannot make the right decision if they skip a crucial piece of information. Adjusters can now recognize claims that require in-depth inspection with the aid of data processing tools. They can recognize them before settling, those that are important are implemented first.

Here are four areas where data analytics can help tackle insurance claims processing challenges:

Subrogation

Instead of recognizing the scam at the later stages of processing, using the AI data management can develop a chatbot during the First Notice of Loss (FNOL) stage and make it to converse with the customer and record their responses. The predictive analysis then monitors the text messages and compares them with similar unstructured data and may report that this interaction is fraudulent based on that.

Insurance Fraud

From hundreds of insurance claims, identifying a fraudulent one is difficult. To settle the claim, expert scam artists devise ways to trick the insurance companies. But, the algorithms can detect false statements with predictive analysis. The research is easier because it is a blend of old rules and modern instruments. The solutions operate on real-time data mining, search-and-exception scenario checking. This data is gathered through all phases of the insurance claim period.

Disputed Claims

To defend the litigation, insurers have to retain a significant part of their company's resources. But conflicts would decrease significantly with predictive analysis. With the aid of data scientists, critical ratios can be calculated by the insurers. The proportion of contested claims and money expended on defense can be computed. Thus, it is possible to recognize the potentially contested claims in advance. More experienced adjusters could be granted these policies. Experienced adjusters will be able to resolve the cases at a higher pace at lower sums.

Settlement

A company's overpayment on an insurance claim is a major loss. It would be a massive disaster for the insurer, given the number of concurrent claims. The settlement will be improved by predictive analysis using an effective machine learning model. The history of the track claims, comparable claims, and other parameters are captured. This helps in an effective way to settle the numbers.

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