predictive analytics

Collecting and analyzing data does not provide business value. Improved business processes that are more accurate and efficient provide business value. Without embedding a predictive model into a relevant business process, all the work and cost put into predictive analytics will be for nothing. Yet the implementation of predictive models has been an afterthought for many insurers. ... Read More >
Business intelligence looks at historical data to provide information about a company’s operations and performance. This can provide valuable insight but has limitations for predicting the future. Simply looking at historical patterns does not tell us how much of what we see in the data is a reliable pattern that can be depended on to continue – what we will refer to as “Signal” – and how much is due to random chance or unknown variables – what we will refer to as “Noise”.  ... Read More >
Data is a purpose-driven asset. It is collected for specific reasons to meet specific needs. The structure of a given set of data is driven by the immediate purpose as well. Data models used to create efficient daily processing are not those used for efficient storing, and yet the structures which facilitate easy access for reporting and business intelligence are different. ... Read More >
Insurance companies are organic institutions that evolve over time. The industry trailblazers in using predictive analytics faced new challenges and had to find their own solutions, but they experienced the benefit of first-mover status. Because the technology available was simultaneously evolving at a rapid pace, competitive advantages were found in investing in cutting-edge expertise and tools. ... Read More >
“All life is problem solving,” said the late philosopher Karl Popper. He was right, of course—especially for those who work in insurance. How can we protect individuals and businesses from increasing cyber threats, such as ransomware? How can we segment claims effectively? ... Read More >
For tech-forward insurers, predictive risk analytics has become key to profitable underwriting in the enormous market for small-business workers’ compensation insurance. But in the age of COVID-19, the competitive importance of these technologies may very well become a make-or-break proposition. ... Read More >
More than most industries, Property & Casualty insurers use data and analytics to make smarter, faster decisions. Insurers are making significant investments in advanced analytics platforms, but what differentiates insurers who are realizing significant value from their investment compared to those who are struggling?... Read More >
Even within P&C Commercial lines, Workers’ Compensation insurance is a unique niche. The stakes, if not higher, are at least different when you’re insuring not buildings and vehicles but human beings. People. Can you imagine a more unpredictable risk, or one that contributes in more ways to your success? ... Read More >
The three main use cases FRISS supports are: FNOL Fraud Detection – detects fraud during creation of the claim. For example, a staged loss or similar previous claim.  Referred to the Special Investigative Unit (SIU) – FRISS provides case management tools which allow the SIU team to assess alerts and manage the claims until its closure. Underwriting – fraud can be prevented by feeding back into underwriting claim history based on inputs like driver’s license.  ... Read More >
Big Jim owns a towing service, and his lot can store up to 100 vehicles. Every time there is a road accident in town, Jim gets a call to tow the vehicle. The vehicle then remains in his lot until he gets direction to transfer it to either a repair shop or a salvage yard.... Read More >

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