How big data is changing the cost of insurance

our consultant managing the role
Posting date: 5/3/2016 8:08 AM
Dave Pratt winced when his teenage son bought himself a Jeep, thinking of how high the insurance would be on a young driver with a flashy car.

But unlike most parents, Mr Pratt is at the forefront of the insurance industry's efforts to change the way car insurance is priced. So Mr Pratt, who works for insurer Progressive, installed the company's Snapshot device in his son's car.

It's what's known in the business as a "telematic" device, which monitors the speed his son drives every second and what time of day he drives. The device also beeps three times when his son brakes too suddenly.

When his son accused him of trying to train him to be a better driver, Mr Pratt agreed that was what he was trying to do.

This, he says, is the future of car insurance: being able to monitor individual drivers to give them lower prices but also to make them better drivers.

"Now that we can observe directly how people drive, we think this will change the way insurance works," says Mr Pratt, who adds that Progressive has more than a trillion seconds of driving data from 1.6 million customers.

"18 year old guys pay a lot for insurance, but some 18 year olds are really safe drivers and they deserve a better deal."

Sloppy business

Car insurance firms like Progressive in the US, to Tesco Bank in the UK and Generali Group in Italy, are currently in a race to convince consumers that letting them monitor their driving behavior is actually a good thing.

This is because the technology, while not new, has only become affordable recently.

Also, consumers are just getting used to the idea of being tracking and having their data collected.

"The way we've done insurance now compared to what we can do is sloppy," says Mike Fitzgerald, senior analyst at Celent, a research firm.

"We're taking tens of thousands of people and saying they all have the same risk profile when in fact they don't.

"Most people are actually overpaying."

This is because traditional insurance looks at averages, as opposed to specifics. You fill out a form saying you're a certain age, and drive a certain type of car, and are a specific gender - and that's basically all the data car companies have to assess your riskiness as driver.

Better big data technologies, like the telematic driving data collected by car companies or even information gathered from social media profiles, can help augment that risk profile.

"If I'm a driver that doesn't drive that frequently, and I have a pattern that would indicate that I drive more carefully than an average person with my profile, then I may be able to save 30-40% on my car insurance, and that's pretty significant," explains Joe Reifel, a partner at A.T. Kearney, a consulting firm.

Social life insurance

It's not just car insurers who want better data on consumers.

Jamie Yoder, head of Pricewaterhouse Cooper's insurance advisory practice, recently wrote a report looking at how sensors - like those used to help monitor workouts - could be used by life insurers looking to evaluate customer health.

And of course, there are the tweets.

"Every insurer we have spoken to has a team that looks at social media. The tools are rudimentary, but the data is being used," says Craig Beattie, also of Celent and an author of a report looking at social media use among insurers.

The challenge is being able to automate those teams, instead of using up precious manpower. "They're sat on a great deal of opportunity with data but they can't get to it," says Mr Beattie.

Denied benefits for smiling

Of course, there is also the danger of misinterpreting that data.

Nathalie Blanchard offers one cautionary tale.

In 2009, while on leave from her technology job in Bromot, Quebec due to severe depression, she went to the bank one day only to find out her health insurance benefits had been cut off.

The cause? A few Facebook photos in which she was smiling.

The insurance company "determined unilaterally that based upon what they saw on the site, they didn't think that she was disabled and they cut her off," says her lawyer, Thomas Lavin. The case later settled out of court.

Anita Ramasastry, a law professor at the University of Washington, cautions regulations will need to be instituted to make sure that insurers don't overstep into big brother territory.

She asks: "are disclosures being made in a way that consumers understand how their social media information is being used for insurance scoring purposes and is there a way for the consumer to do anything about that?"

Fundamental shift

For now, using big data analytics for insurers is still in the early stages.

Only 2% of the US car insurance market offers an insurance product based on monitoring driving, according to A.T. Kearney's Mr Reifel.

But that proportion is projected to grow to around 10-15% of the market by 2017. And other countries, like Italy and the UK, are already using the data to analyze not just risk profiles but also to determine who is at fault in car accidents.

The future, most analysts agree, is to create a continuous feedback loop between insurers and consumers, so that consumers will react to the big data analyzes that insurers do on them and change their behavior accordingly.

"That's the end game - insurers could not just cover losses if they occur, but actually prevent them," says Mr Beattie.

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

HOW TO MAXIMISE YOUR TRAVEL DATA WITH THE RIGHT ANALYTICS

Summer is here and over half will be planning their holiday via mobile phone. Heathrow Airport, one of the busiest airports in the world, will be filled to the brim with eager travelers. Hotels and cruise ships will be bursting at the seams. But, will the people traveling today, be the same people traveling next summer? How will the travel organisations know?  They’ll need data analytics to help them make sense of all their data with advice and direction on how best to utilise nuggets of actionable insights.   It wasn’t that long ago, at the busiest time of the year, Heathrow, as well as most of the airports around Europe, shut down. Why? Because no had planned for something of that magnitude – the snowstorm of the century some said. Tangible evidence of planning for every scenario, no matter how unlikely.   What Would a Data Professional Do?  Data professionals ask questions. They create scenarios. In the above instance, what might a Data Engineer have asked? In a data-driven world, could the airport shutdowns have been prevented? Would scenarios and algorithms have helped to scale corrections faster?  These are just a few of the questions a data professional might ask. But, in order to provide solutions, first you need a builder – a data engineer. You don’t always have to build from the ground up, though. Sometimes, reconstruction and refurbishment are just what’s needed to bring a project back to its former glory.  Digital Transformation in Travel  As travel companies prepare for the upcoming Digital Travel Summit at the end of this month, they’ve released a report targeting certain areas on which to focus. Not the least of which is the need for stronger data analytics with less than half reporting they have a plan in place but are looking to improve it.  One of the main areas of focus according to the report is lack of programming skills coupled with a hesitant approach by management. The complexity and difficulty of trying to analyse and personalise the sheer amount of consumer data available is also a factor.  And without the right tools, it’s even more difficult. Knowing what data is useful and what is isn’t is a major challenge, as well as the ability to track large amounts of data in real time can be a daunting task. These challenges and more, offer the perfect opportunity for a seasoned Data Engineer to step in, and take on a leadership role.  Take the Next the Step  Are you a Data Engineer ready to take your career to the next level? Are you a wizard at big data technologies with a leadership bent? Then we may have a role for you. A leading e-commerce company is looking to transform their data infrastructure (Python, AWS, Airflow) by hiring a senior data engineer.  Check out our other current vacancies or contact Joshua Carter, Recruitment Consultant with a Data Engineering focus at +44 20 8408 6070 or email joshuacarter@harnham.com to learn more.

Route to the role of Data Engineer

Do you like breaking things down to see how they work? Do you want to build something that helps solves problems and can make lives better? Are you a puzzle solver curious about the world around you with a knack for mathematics? Do you prefer to work behind the scenes or front of stage? If you want to be the person behind the curtain, then this is your year. The year of the Data Engineer is here.  In last week’s article, we talked about Data Engineer as the unsung hero of the data science world and briefly touched on route to the role of engineer. Though experience supersedes education, you do need the basics – a bachelor’s degree in computer science, data science, applied math, physics, statistics, software/computer engineering which can lead to a Master’s in Data Engineering and to cement your knowledge – fellowships and professional organisations are now available around the world. In today’s article, we’ll cover a few options.  Lay Your Educational Foundation Computer Science, Data Science, and Engineering programs abound in university today, but no school can really teach big data skills. It’s too focused. Most schools today offer general purpose tech education with a focus on web development or backend systems. And here begins that Catch-22. Though experience supersedes education, you still need a framework from which to build.  More often than not, if you type Data Engineer into Google looking for education programs, you’ll get undergrad opportunities for Data Science. However, that’s not to say a Bachelor’s in Data Science can’t lead you to a Master’s in Data Engineering. So, how do you get from point A to point B? Here are a few suggestions:  Beef up your skills with specific certifications for the languages businesses need – Scala, Python, and Java  Take courses in data engineering technology: Hadoop, Spark, AWS, GCP, Azure etc.   Join a professional organization for Data Engineers such as The Data Warehousing Institute  (TDWI) or the Institution of Engineering and Technology (IET) – here you’ll find articles, resources, and a network of mentors ready to offer advice and suggestions.  Apply for a fellowship  with ASI Data Science – an 8-10 week intensive project with one of their partner companies to solve real-world business problems using Data Science or Data Engineering skills. If you’re a postgrad or higher, this a perfect opportunity to build your portfolio.  Boost Your Data Engineering Resume with These Tips In the world of data engineering, it’s important to highlight the details. Be specific: Companies will be more interested in interviewing you if you can clearly outline why/what you have used different technology for. Keep this punchy and concise, and outline your in-put with said technologiesOutline projects you’ve worked on Detail the technologies you’ve used  David Bianco, a Data Engineer with Urthecast, offers the following advice to data engineering students. Be fluent in the languages and tools you use to get the job Understand the concepts behind what you’re doing Get involved with a community – meetup.com, hackathons, and other groups in your area are great places to get started.  If you’re interested in switching your career  to Big Data, check out Jessen Anderson’s new e-book, The Ultimate Guide to Switching Careers to Big Data -- Upgrading Your Skills for the Big Data Revolution.  Your Turn: Route to the Role of Data Engineer Our data driven world moves at lightning speed and it can be hard to keep up. If you’re a Data Engineer, we want to hear your story.  What was your route to the role? What kind of cross-training programs might businesses and schools employ for future Data Engineers?    What other backgrounds are we overlooking as businesses seek to find and engage this most critical role within their data science teams?    What can we, as recruiters do to engage qualified candidates ready for their next role in the world of data and analytics?