This guest post is by Ingrid Walter, who is a blogger and freelance writer.
Big data started as a way to describe data sets that are too big for traditional databases to capture, store, manage, and analyze. But now the term has evolved to define a set of technologies that can solve complex problems by analyzing trends from large and variable data. Big data is already commonplace in industries like banking, construction, retail, and transportation. One of the latest industries to take advantage of big data is motor insurance, which is using the modern technology to provide a more personalized service to customers.
What if you can accurately asses risks?
On Ferguson Insurance Center we have a list of the most common car insurance mistakes you can commit. Using big data, insurers can now create a better picture when it comes to claims. Forbes notes that risk assessment in car insurance is what the industry is built on. Some insurers now use “telemetry-based packages” that feed data direct from the vehicle to their system. This allows the insurers to get an accurate record of a driver's behavior, which can then affect their assessment of an accident or theft.
Easier application process
ZhongAn Online P&C Insurance, the first internet-only insurer in China, is a great example of how big data is making the application process easier. They are using machine learning and analytics to analyze data. Wang Yu head of car insurance at ZhongAn told the South China Morning Post: “We have broken the online purchase process into 45 parts. We monitor and analyze data flows from each part and if we notice that users spend too much time in one part, then we know something may be wrong with it or it has potential to be optimized.” Don't be surprised to see this method of application spread across the globe.
Fairer risk assessment
There's an ongoing debate about whether or not insurance companies should use a consumer's credit score as a way to assess risk. While credit scores have helped companies assess a consumer's personal responsibility, the Insurance Journal points out it isn't popular: "it’s never been obvious to the typical consumer what one’s credit history has to do with one’s likelihood to get in an accident". With big data insurance companies have even wider scope to determine credit scores by providing even more variables. However, the article also notes that the wide range of personal information that could be included in the data scan, such as social media accounts, is likely to put off many customers.
Better fraud detection
In the US, insurance fraud accounts for $80 billion a year. Fraud also makes up about for about 10% of the property-casualty insurance losses and loss adjustment expenses each year. To battle this, 48 states, including the District of Columbia made insurance fraud a specific crime.
Most insurers have to manually assess and report suspicious insurance claims before they can verify an actual fraud. With big data, specifically predictive modeling and strict data management, insurers can quickly identify when there is a high-risk case that needs investigation. They can simply match variables in claims against profiles of past fraudulent activities.
This type of protection can be seen in the trucking industry. Verizon Connect explains how advanced fleet tracking software helps with insurance claims in the event of stolen assets. This is mainly because every part of a truck’s journey is recorded, and insurers can see “exactly what happened where”. Monitoring high-value assets has never been easier. Driver ID technology comes in handy, too, allowing the right people to take responsibility for any loss in company assets.
The bottom-line for the motor industry is: if every data generated is traced, tracked, recorded, and stored, insurance claims can be easy to handle—both for the customer and the insurer.
Exclusively written for fergusonins.com by Ingrid Walter