Analytics solutions designed to handle the volume and variety of data available today also help insurance companies improve catastrophe risk modeling, through which companies determine the exposure of current policies and predict the probable maximum loss (PML) from a catastrophic event.
Motor insurance policies are traditionally priced on forecast risk - using rating factors such as number of miles you might drive, where you live, your age, the engine size and what you will use the car for - customers then pay a premium based on these values - however the introduction of
When was the last time that you looked at one thing? Just one thing, nothing else? We seldom look at just one thing, but rather look at ‘a thing’ in terms of its relationship with ourselves and with everything around us and it. Even in looking at a photograph, don’t we also think of the photograph
Solutions for analyzing big data can play a critical role in addressing the increasing prevalence of claims fraud. Traditionally, fraud is estimated to account for approximately 10 percent of insurance company losses, and that percentage is rising. Insurance companies need ways to quickly
Visitors to the London-based Chartered Institute of Insurance are able to visit the small museum, which includes policy documents and ‘contracts of insurance’ going back 300 hundred years,
These documents are, in numbers of years, as far away from Gutenberg’s printing press of 1439 as we are today
In my previous two blogs [here and here], I’ve talked about how cognitive computing and big data present the insurance industry with great opportunities and some challenges. To develop strategies that capitalize on the potential gold mine of information that big data represents, many carriers will
Almost my whole working life has been spent in the insurance industry, and in the early days, we thought we were being ‘cutting edge’ when using fax and telex. Now, after 30 years, the insurance sector is sitting in the centre of a new industrial revolution called ‘analytics’.
“Organizations around the world lose an estimated five percent of their annual revenues to fraud, according to a survey of Certified Fraud Examiners (CFEs) who investigated cases between January 2010 and December 2011. Applied to the estimated 2011 Gross World Product, this figure translates to a
This morning I read this post on Toolbox
Self-Service Business Intelligence is…
- A win-win for IT and business professionals
- A trend that could possibly threaten some IT professionals’ livelihoods
- A disaster waiting to happen…sounds like a reporting nightmare
What do you think and
What if insurance companies could simultaneously improve customer satisfaction, retain valuable policyholders and maximize cross- and up-sell opportunities?
To achieve these goals, insurers need ways to anticipate customer needs and determine the next best action for each individual customer.
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