Role of Real-time Analytics in the Insurance Industry

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The insurance industry is advancing and the businesses are looking for ways to differentiate. As the industry is competitive with limited differences in the features of various products, the real-time analytics is needed to offer new ways to keep the customers satisfied. This way, the customers get a personalized experience and they make faster decisions.

The online channels of customer interaction and social media conversations have created new real-time data. The real-time analytics are leverages by the high technology and online businesses that create personalized experienced to the customers. As the insurance is a life time value business, it can gain a lot through real-time analytics.

Get to know the opportunities as well as challenges that are faced by the insurance companies in the real-time analytics.

Real-time analytics help the insurance firms deliver great value to the customers by responding quickly to their events. Here are some cases of real-time analytics in the insurance industry.

When a prospective customer visits the website for a quote, the real-time analytics is used to predict the tendency of the customer to go away without applying for a quote. This can also be used to generate interventions such as a free consultation.

It is also known to fast track the claims improving the customer significantly. However, the fast-tracking of the claims can augment the risk of fraud. The real-time analytics will also decrease the risk of fraud though the processing of claims is accelerated.

Basically, in all these scenarios, the real-time analytics play a role in processing the data as it arrives instead of storing and recovering it later for the purpose of analysis.

The real-time analytics require faster decisions that have a great impact. The time available for a decision is based on the situation.

The real-time analytics can be done in many forms. These forms are a preset deterministic rule, combination of rules and analytic scoring models that is based on the self-learning method.

In the preset rules form, the analytics automated decisions are taken depending on the preset business rules that are taken in a deterministic way. For instance, the online consumer problem is solved with a pop up chat window that helps the customer to get the issue clarified within a few minutes.

The pre-determined model will capture fraudulent claims in the industry. It is based on the prior statistical learning model to identify the claims. Once the claim is raised, it will be identified to be fraud based on the generated scores by the model. In the self-learning model, there is a necessity to improve continuously and adapt the model as the patterns might be changing continuously.  It is good to explore the customer touch points in order to identify the application of the real-time analytics for any organization.

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