Companies that insure our road vehicles request information including the driver’s age, gender (no longer legal in Europe), claims history and the ZIP or post code where the vehicle is parked at night. On this narrow data set, insurers construct an analytic model used to assess and price risk. A
In last month’s post, I talked about how cognitive computers, like IBM Watson, have the ability to do what the earliest underwriters did: approach each risk individually and, based on historical learning, apply reason and judgment to determine a rate. Cognitive computing allows insurers to analyze
Today, two major factors are poised to change the insurance industry in a way it hasn’t seen in more than 50 years—emerging capabilities enabled by cognitive computing and big data, and an empowered consumer. If history is any indication, these technologies will usher in a new paradigm for the
Insurance companies are looking to accelerate the speed and increase the precision of catastrophe modeling, the process through which companies determine the exposure of current policies and the probable maximum loss (PML) from a catastrophic event. Catastrophe modeling is vital for setting policy
Historically, the insurance industry has simply accepted the staggering cost of fraud as a cost of doing business. Now, however, insurance fraud is on the rise. But help is at hand. Next-generation fraud solutions and big data are changing the equation in favor of insurance companies and their
Interest is growing among insurers for big data solutions that can help them identify, monitor and manage fraud in their underwriting and claims. Advanced fraud detection and management solutions are helping P&C and life insurers reduce losses by improving the precision and timeliness of fraud
Did you resolve to learn more about big data in 2013? Boost your learning by checking out some new resources you may have missed on IBM Data Magazine. Some of our very own Big Data Hub authors have published pieces on big data topics and solutions.
Claims fraud is a serious issue for insurance companies. Estimates are 10%-20% of claims are fraudulent. That costs the companies major money, and in turn, it causes costs to rise for consumers. Kim Minor, insurance industry marketing manager at IBM, explains how leading insurance companies are
While “a single version of the truth” might sound reassuring, relying on such a strategy can seriously impede real-time businesses. For many missions, it’s time to embrace a plural version of the truth.
Insurance claims fraud is estimated to account for at least 15 percent of insurance company losses, a cost that impacts the bottom line of every insurer in the industry, and ultimately, consumers as well.
Insurance Bureau of Canada (IBC), Canada’s national insurance industry association for home
Individual fraudsters and organized rings are taking advantage of favorable regulations, overworked adjusters and investigators, and a clogged court system to increase the rates of insurance fraud. Learn what insurance companies are doing to "Outsmart the Insurance Claim Fraudster" in this webcast.
Leading organizations in financial services, telecommunications, retail, healthcare, digital media, insurance and other industries are outperforming their competition by generating new, actionable insights from big data. There are three dimensions in their performance that indicate a correlation
Organizations today are collecting tremendous volumes of data, generated by a wide variety of sources, often at extreme velocities. This is “big data”—the millions of stock trades, call detail records (CDRs), social media posts and patient test results produced every single day. Leading