satyamkapoor

I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First. ...

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I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First.

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Machine Learning, Analytics & Big Data are about to change Insurance

By satyamkapoor |Email | Jan 17, 2018 | 9138 Views

It is largely data that has determined success, failure and change in the insurance business. However, today is different. The advent of big data technology, advanced data analytics & machine learning are changing the game entirely. Now, the winners are those who are capable of accessing the most relevant data, analyze it in new & unique ways, and apply it at the right time and place with a swift pace.
These new technologies are allowing companies to improve performance as well as helping insurance carriers to identify new areas of opportunities & risk.

The evolution of auto policy rating is a ready example of how more and better data, used well, leads to improved risk analysis and pricing. Historically, age, marital status, and driving history were used to price a policy. Over time, other data such as credit scores and good student discounts were included.  Carriers now have the opportunity to include driver behavior data, captured directly from vehicles through telematics devices, to further improve pricing accuracy.

Carriers are also using new sources of data to proactively mitigate risk, helping to minimize loss, or even preventing it altogether. Use of information from water detection sensors in the home to stave off significant damage or flooding, or from devices worn by truck drivers, miners and other employees to monitor alertness, are just early indicators of the extraordinary preventative value inherent in the combination of data, devices, and analytics.

As access to data from sensors and connected devices expands, property and casualty (P&C) carriers also have increasing visibility into consumers' lifestyles, patterns, and preferences. Layering analytic solutions and machine learning technology on top of that, carriers can quickly mine that information for new areas of risk, and new coverage opportunities.

It's clear, for example, that there is an increasing need for more non-traditional coverages-as-needed, those linked to personal or time-and-place circumstances, and personal/commercial hybrids, to name a few. I call this sort of policy personalization "insure me" coverage.

As a simple example, though the days of buying travel insurance at an airport kiosk or from a brick-and-mortar travel agent are all but gone, the proliferation of online data and transactions has brought with it travel insurance 2.0. As consumers research travel or purchase airline tickets, hotel rooms, or rental cars, insurance coverage can be offered up digitally, exactly when customers are most apt to consider it. 

The growing sharing economy, combined with the availability of more and better consumer behavior data (and our continued willingness to give it up online), is also ushering in the need for personal/commercial hybrid policies. As companies like Airbnb expand, homes become part-time personal residence and part-time commercial property. Similarly, vehicles driven by Lyft and Uber drivers are used for both personal and commercial use. Applying real-time data and analytics, hybrid coverage could switch back and forth based on how the vehicle or home is being used, ensuring that the policyholder has the correct coverage at all times.

For another example, consider that most US auto policies today provide limited coverage for policyholders traveling to Mexico. A separate policy, specific to coverage in Mexico, is needed for those who commonly travel to and from Mexico. Now imagine a policy that would automatically extend coverage if a motorist drives into Mexico, and end that coverage when they leave, providing coverage on an as-needed basis.

Data has always been a vital factor in the insurance business. For hundreds of years the industry has used the past to predict the future. Carriers have used patterns, events & behaviors to predict future likelihood and create informed pricing & risk models. Although, these metrics can be useful today, the newly found precision, volume & timeliness of data - in combination with the increasingly powerful technologies - are actually shaping the future of the industry. They are helping predictably improve businesses, mitigate risk, and develop the exact policies that are needed. 

Source: HOB