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Author: Papitha Mohan |05/04/18

Advanced Analytics in Personal Insurance

Reaping benefits out of data investments!

Technology has quickly progressed, for insurers to use real-time data in a way that is both meaningful to customers and profitable for companies. All the investments that go into building the data store of the enterprise needs to pay off and analytics is the way.

Analytics image

While actuarial analytics is the essence to an insurer’s profitability and sustainability, technology has shown innovative ways to put data into use other than traditional use cases of new product development and pricing.

Sourcing the sources

Long gone are the times where limited sources of customer data were his application and a few statutory bodies. Data is surging in to the market from absolutely surprising, least expected sources. It’s up to the insurers to pick the data and put it to use. Some of these sources are shown in the below image

Data Stores image

Social Media is the most important of the modern data sources and can make significant contribution to the personal line carriers. Excellent predictive models for both home and auto line risk assessment can be built around customer personal information furnished on the web like marital status, interests and hobbies or life events.

Like discussed in our article on IoT for personal line carriers, the connection between customer devices and continuous flow of information can not only provide all risk details but go a long way in mitigating incidences by predictive alerts

Third party from web can give details about customer buying behavior, browsing data, products and competitors in the consideration set. A vivid example of such data could be from price comparison websites. This data can be used for descriptive models to understand the customer needs and manage experience accordingly.

Smartphones can provide data about intensity of mobile phone usage, browsing, which could be sourced directly from smartphones. Most visited places (by location tags) and the associated times of day can also be fetched from smart phones.

Telematics is a key source of data for auto insurance that can provide data about driving habits like when, where, how far and how smooth one drives.

Data helps you hit the nail

  • Sales and Marketing

With virtually, knowing in and out of customer wants and habits, there is no excuse to market a wrong product or at a wrong time. Increased targeting through display ads on and off Facebook can yield 50 to100 percent higher returns than from the average Facebook audience. Analytics can provide improvements in engagement and conversion, measured in net promoter score, acquisition, and lifetime value.

  • Predictors of claims susceptibility

The analytical models that could be built around risk data can help make accurate underwriting decisions. They can be used to predict the risk value and assign prices thereby replacing an underwriter in cases that are considered to be less risky. Increased Straight Through Processing (STP) benefits in increased business and enhanced customer experience by quickening the turnaround time

  • Specific risk based pricing

One price fits all is no longer acceptable to the customers. Customers are willing to share private data like daily driving details to enjoy discounted pricing. Analytics helps satisfy the need and models can be built for pricing based on specific risk details. This is on the rise in personal lines, especially in telematics based auto insurance.

  • Better managed customer experience and service

The most important benefit of turning to analytics is managing digital customer expectation. The digital disruption in insurance warrants a flawless journey for a customer with delightful moments where customer expectations are exceeded. To provide such superior experience it becomes essential to harness customer data and predict customer actions.

What is of such immense value won’t come without its own challenges. Below image shows the impediments in the data journey.

Analytics in Insurance

Buckling up with solid technology to tackle data issues and building analytical models that learn is the opportunity to succeed for a digital insurer.

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