Search Results: INSURANCE (510)

Stuart Rose 0
Time is precious, so are your analytical models

The analytical lifecycle is iterative and interactive in nature. The process is not a one and done exercise, insurance companies need to continuously evaluate and manage its growing model portfolio. In the last of four articles on the analytical lifecycle, this blog will cover the model management process. Model management

Stuart Rose 0
Putting predictive analytics to work.

Insurance relies on the ability to predict future claims or loss exposure based on historical information and experience. However, insurers face an uncertain future due to spiraling operational costs, escalating regulatory pressures, increasing competition and greater customer expectations. More than ever, insurance companies need to optimize their business processes. But

Analytics
Stuart Rose 0
Demystifying analytics

There is no doubt that analytics is an overused and often abused term. So what does really analytics means? In part 2 of a series of articles on the analytical lifecycle, this blog will highlight some of the common and emerging techniques used to analyze data and build predictive models

Peter Chingos 0
Moving to value-based health care with episodes of care

The move to value-based payments is well underway and accelerating. The shift is putting unprecedented pressure on health care providers to better manage the cost and quality of care they deliver. Who will have a much better shot of success? Organizations that understand how well they are performing, where they have opportunities to improve

Advanced Analytics | Analytics
Mike Gilliland 0
Guest blogger: Dr. Chaman Jain previews Winter issue of Journal of Business Forecasting

JBF Special Issue on Predictive Business Analytics Dr. Chaman Jain, professor at St. Johns University, and editor-in-chief of the Journal of Business Forecasting, provides his preview of the Winter 2014-15 issue: Predictive Business Analytics, the practice of extracting information from existing data to determine patterns, relationships and future outcomes, is

Data Management
Stuart Rose 0
Data is King

In my last blog I detailed the four primary steps within the analytical lifecycle. The first and most time consuming step is data preparation. Many consider the term “Big Data” overhyped, and certainly overused. But there is no doubt that the explosion of new data is turning the insurance business

Analytics
Stuart Rose 0
The steps to using analytics…successfully

Advances in technology, evolution of the distribution channels, demographic shift, economic conditions and regulations changes. How does an insurer prioritize all these seemingly competing goals and create sustainable competitive advantage. One answer is analytics. Many insurance companies are just beginning to take steps toward becoming an “analytic insurer” – one

Stuart Rose 0
Data governance - the new prodigal child

The old adage is that “Data is the lifeblood of the insurance industry.” However, for many insurance companies, data is like the red-headed stepchild. No one is willing to take care or have responsibility for it. In the past, insurance companies have created data governance programs, but these have often

Data Management
Stuart Rose 0
Welcome to “data-driven decisions”

Business analytics is about dramatically improving the way an organization makes decisions, conducts business and successfully competes in the marketplace. At the heart of business analytics is data.  Historically, the philosophy of many insurers has been on collecting data, data and more data. However, even with all this data, many

Customer Intelligence
Stuart Rose 0
Is the customer experience overrated?

According to analyst firms, consulting companies and various other research, customer experience is the primary priority for insurance companies.  But is customer experience overrated? Let’s start by considering the primary interactions between an insurance company and its customers: new business, billing, renewals and claims. Ask any insurance executive, especially property

Programming Tips
Erwan Granger 0
Cloud: 4 deployment models

This is the last of my series of posts on the NIST definition of cloud computing. As you can see from this Wikipedia definition, calling anything a “cloud” is likely to be the fuzziest way of describing it. In meteorology, a cloud is a visible mass of liquid droplets or frozen

Simon Kirby 0
The secret to successful underwriting

It is clear from many of the comments arising from this year’s Insurance Times Broker Service Survey that brokers value underwriters who are open-minded and willing to adapt wordings and terms where possible to accommodate clients’ individual needs. They also value a close dialogue and partnership with underwriters who are

Simon Overton 0
There’s a need for speed, but with an eye on fraud

According to broker’s “best” and “worst” verbatim responses in this year’s Insurance Times Broker Service Survey, insurers who act quickly to verify and settle claims are their preferred partners. This year’s importance scores reveals brokers are placing a stronger emphasis on claims in comparison to previous surveys. Those instances where

Stuart Rose 0
Can I Quote You on That? - 2014 in review

Earlier this year, I was speaking with an insurance executive and he said something that turned out to be my favorite quote of 2014: “Premium revenue is like heroin.” While this seems like an unlikely analogy  or simile?. The point this executive was trying to make an interesting argument.  Insurance

Analytics
Jon Lemon 0
Four-step approach to government fraud detection

Every day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits,  health care, food stamps, tax collection,

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