Insurance Analytics

How Machine Learning is transforming the Insurance industry

For Risk Managers, taking chances simply is in their DNA. New systems aren’t adopted unless everyone is 1000% certain that the move will be safe, trusted and solid. The benefits of big data, however, have become so apparent that insurers who aren’t following through on the opportunities and advantages that it holds, risk losing their competitive edge. The “must act now” scenario for insurance companies to start using big data solutions have become more important than ever, lest you become irrelevant in this ever changing market.

How insurers are deriving benefit from Machine Learning 

Many insurance companies who are leading the way in the U.S. insurance sector are scrambling to increase their use of big data analytics and industry best practices. Vice president of Capemingi Finance Service’s insurance business unit, Seth Rachlin, who has been in the insurance industry for a quarter of a decade, says that while insurance companies have historically been slow adopters, he’s never seen an industry jump to change its business model so quickly.

“The pace of change frankly in the past two to three years is something I’ve never seen before within the industry,” Rachlin told Datanami.com before adding that the industry quickly became much less cautious of big data analytics when they realized that insurance companies who are achieving top results were using cutting edge big data solutions. This realization led to a ripple effect of companies scrambling to ramp up their data analytics usage.

“We’re seeing a tremendous desire to leverage technology broadly, and data more specifically. The business is getting it, and the business is wanting to act on it. And I think there’s even a level of fear of being left behind,” adds Rachlin.

Data analytics started to play a larger role in the insurance industry as a result of how auto insurers started using the intelligence that it provided. In the past, insurers would price policies based on less than 20 variables of a customer such as the gender, the area where they were driving, their driving track record and the age of the driver, for example. There was a standard list of questions that each potential client had to complete in order to calculate the insurance policy price that the auto insurer would offer.

A small number of auto insurers started taking this information gathering process to the next level by sourcing loads of additional data about a potential customer. Everything from reputational data from Yelp and a customer’s credit score would be used to create over 1000 variables that the auto insurer could use to determine policy prices.

The multitude of data that these insurers were able to generate allowed them to create much finer ratings classes and provide more accurate and targeted pricing strategies. It enabled them to conduct more accurate calculations and reduce their risks exponentially.

Person-centric vs. Claim-centric false claim detection 

Some of the ways that advanced analytics can help insurers manage risk is by detecting false claims and preemptively thwarting fraudsters by predicting the potential for fraud. How big data enables this is by providing person-centric fraud detection techniques instead of claim-centric detection techniques.

The reason why insurance companies are able to use data and machine learning to determine customer acquisition and manage risk is due to the advances in statistical modeling techniques that are available to companies. With a person-centric approach, the beneficiary’s claim history and behavior across multiple sources (such as using a person’s social graph to find similar behavior patterns among individuals that he or she is connected to, and similar claims that were reported by the same person) are analyzed.

“You need to know a lot less going in [with] a lot of the statistical modeling techniques that are being used today. You can kind of throw everything in and see what works, whereas statistical practices 15 to 20 years ago, you needed to have formal hypothesis about why these things matter in order to actually get results out of the model,” says Rachlin.

Better customer acquisition 

New customers are also assessed more accurately thanks to big data, which means that insurers can adjust their premiums more effectively and customize products for their clients. Big data can also improve the customer service that you are able to deliver, which is a huge marker of a successful company in the industry.

Customer retention and satisfaction can be greatly improved by being able to gain deep insights regarding their customers’ behavior patterns and needs. Big data helps you improve the process time for claims that are approved and above board, reduce annual financial losses and thereby gear your insurance company for the future. 

Today’s most successful insurance companies know that they are data dependent. Regulations, such as Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations are an additional push that has insurance companies committing to gaining a more thorough knowledge of their customers.

The main challenge that many companies face when starting to research and use data analytics tools relates the diversity and variety of data that their business generates. Data in the insurance industry, for example, comes in so many shapes and forms.

Everything from the information obtained during the customer on-boarding documentation process to customer care call logs and browsing patterns on customer facing websites that could indicate customer tendencies to geo-spatial data and social data needs to be integrated and aggregated in order for it to be useful to an insurer.

Tellius offers an advanced analytics solution that enables insurance companies to leverage operational intelligence through advanced machine learning and data exploration technologies on a single, powerful platform. Our distributed data processes enable you to collect, converge and interrogate data assets at lightning speed for real-time business intelligence so that you can spend less time on analyzing and more time on execution. Instead of navigating and analyzing, you can simply search. Contact us for business insights and download our eBook on 6 signs your analytics platform isn’t future proof.

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