The Big Data Effect on the Insurance Industry

Big Data and Insurance Sector

Insurers able to leverage analytic technologies to make sense of the growing amount of internal and market data available ultimately will gain competitive advantage.  

– Pricewaterhouse Coopers L.L.P

Insurance is an unique industry, its entire foundation based on the concept of risk. The cheerful basic premise (as you know) is to take out policies as cover for untoward happenings. A financial cover is thus provided to the policy holder, based on cost assessment of the claims.

According to statistics, big data & analytics was the most popular insurance industry buzzword in 2015. More 2015 figures – Insurance companies are looking to gain in operations, improve core processes, fight fraud and other business issues. Here’s a run-through on how the insurance industry is evolving, thanks to big data.

Industry Transformation Signs

By 2020, over 25% of US auto insurance revenue is expected to be garnered by telematics. 

What factors will influence your annual insurance premium is a controversial big data effect that is set to change the insurance industry, for better or for worse. Here is a selection of real-life examples.

  1. As stated in the SAS white paper (Telematics: How Big Data is Transforming the Auto Insurance Industry), two data analytic-influenced auto insurance options could be in use in the near future. PAYD or Pay-As-You-Drive charges customers on the covered miles/kilometers. Hollard Insurance, a South African insurance firm is already using this option, with an additional six mileage alternatives to choose from. PHYD or Pay-How-You-Drive, unlike PAYD, uses telematics to monitor speed, cornering, acceleration, geographic location,lane changing, fuel use and braking. In case of an accident, the insurance company has the luxury of accident recreation, thanks to the data. Read more about telematics usage in the auto industry here.
  2. Behavioral analytics and genetic profiling will determine if an insured person’s ill-health can be attributed to genetic factors or an unhealthy lifestyle. These factors will affect your life and health insurance claims. As more information is gathered, it might be decided that those not used to exercising regularly may have to pay higher premiums. Information gathering sources will include – body sensors to monitor food intake and alert the insured person, data gathered from gym machines, a person’s social media revelations on health/food, record of junk food purchases, etc.
  3. Property insurance companies are also working with telematics for data-usage based home insurance. Their data sources are expected to be security cameras, resident occupancy-tracking sensors, appliance usage records, leak and flood-detection sensors, etc. Similarly, predictive analysis can help insurance firms avoid huge claim risks, by predicting theft or hurricanes, for that matter.

Insurance Fraud 1

Fraud Detection

Data and analytics have been cited as key strategic measures by 82% of insurance executives. By 2018, 25% of the top insurance firms would have adopted big data for either security or fraud-countering purposes. Meanwhile, 80% of claims data is still unstructured. 

Insurance fraud is no joke, it can lead to losing savings, significant job losses, higher premiums and bankruptcy. According to FBI, annual insurance fraud costs extend over $40 billion. Coalition Against Insurance Fraud (CAIF) figures show that planned insurance fraud accounts for $80 billion data (in USA, alone) every year. Fraudulent claims (excluding health insurance claims) costs an average US family $400-$700 annually in increased premiums.

Big data is helping bring down fraud to a large extent. The industry is using behavioral, predictive, text, link, graph and pattern analysis techniques to detect fraud. Post a devastating hurricane, a prominent insurance company was flooded with thousands of claimant documents everyday. Then came the second data flood, via phone calls, social networking websites, SMS, blogs and fax. A Big Data-fueled set up was then created, all incoming claims stored at a central location. An automated prioritizing solution ensured sorting out genuine claims on the basis of evidence that showcased major damage proof and truth on part of the claimant. The insurance company was thus able to pass prioritized claims to the right people, adding to the company’s sales and brand image. Read more about big data’s role in detecting insurance fraud here.

Social Media Interpretations

By 2018, 20% of insurance business will be generated through the Internet. By the same year, 11% business will be gained through mobile channels.50% insurers consider mobile and social media as a priority. Statistics also reveal that customer needs are rapidly rising. The industry is lagging behind in catching up with fast and evolving technologies.

An often-mentioned big data advantage across sectors is customization. Getting policies at competitive premium prices is a huge plus for any potential customer. Lately, customer communication occurs over the phone or Internet, personal interaction is usually limited. Social media is a great medium for day-to-day promotion and monitoring. Selling opportunities can be aptly identified and implemented, thanks to real-time analytics. Identifying customers who are most likely to cancel their insurance policies is possible, thanks to data comparison with previous ex-customers. Insurance firms, banking on similar indicators can attempt to change the concerned customer’s impending decision. They could offer special discounts or premiums or exclusive service and thus retain the customer. Social media is thus used to gather new potential clients and retain current customers. Also, planning marketing campaigns to go with popular/trending events makes for a great sales pitch. Read more about social media’s prominent influence on the insurance sector here.

The Importance of Analytics 

In 2015, 41% insurance companies were still trying to figure out the value of big data analytics. The major applications of big data in the last year were in advanced analytics, telematics, visualizations and claims analytics.

Matthew Josefowicz, President/CEO, Novarica, (a research and advisory firm) stated at an insurance leadership forum – traditional insurance process was made for an information-scarce world. The sector was now adopting to “information super-abundance.” The only way to counter this sudden flood of data was in taking aid of data analytic tools.

To cite a real-life example, American Family Insurance uses a analytics software, Talk & Learn to improve customer data understanding and post suggestions about services and products. Justin Cruz, the company’s VP for strategic data and analytics has stated that providing customer value in a more rigorous and focused manner has been possible, thanks to available data.

Insurance related data tools are evolving all the time. Firms now exactly know what customer section they are catering to, who are least or most likely to file a claim, among other things. This helps insurance firms focus their priorities on other customer-engaging aspects too. Customer behavior is further predicted based on health, bank account, driving records and other related data. Charting out policies and premium rates, based on individual customer needs and risks will be easier.

Summary

A major data gathering debate is the issue of customer privacy. Laws, regulations and rules of each country will determine just how far insurance companies can go. Passing on personal data chunks to a private company is understandably a major customer concern. What will be the other factors that shall determine how big data analytics will shape the insurance industry? Only time will tell.

(Statistics and images sourced from 2015 Novarica Statistics, www.slideshare.net)