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New Approaches, Old Method: Predicting IBNR with Machine Learning
Multiple September 16, 2021 Posted in: Blog, General, Predictive Analytics

Machine learning is a branch of artificial intelligence (AI) that teaches a computer how to analyze and find hidden patterns in data through the use of algorithms. It’s been called a “revolution,” and from self-driving cars to health care, it has begun to change the way we live our lives. Our Pinnacle University group explored the emerging world of machine learning and how it fits into the insurance industry.

Model Monitoring--Can You Afford Not To? Part 2
Greg Frankowiak August 06, 2020 Posted in: Blog Posts, Predictive Analytics
Depending on the number and complexity of models that exist for an insurer, model monitoring runs the risk of becoming overwhelming very quickly. A first step to building a solid model monitoring program is to catalog models in use, including any state-specific versions and assessing the relative importance of each. That could be assessed in a variety of ways including by the number of policies that a model potentially impacts, or the premium volume that a model has influence on. An insurer may also want to assess the complexity of the model and its potential stability. Combining all of the different characteristics helps lead to determining which models are most important to monitor first (relative priority).
Model Monitoring--Can You Afford Not To?
Greg Frankowiak July 16, 2020 Posted in: Blog Posts, Predictive Analytics
Continuing our previous deeper dive into certain aspects of the Modeling Lifecycle concept, this installment is the last entry in the lifecycle—model monitoring. While monitoring is the last step in the process, it is arguably one of the more important steps since it can send a modeler back to nearly every other earlier step in the lifecycle. However, model monitoring often receives the least attention.
Model Implementation—Begin With the End in Mind
Greg Frankowiak June 17, 2020 Posted in: Blog Posts, Predictive Analytics
Last year I wrote about a concept called the Modeling Lifecycle (Modeling Lifecycle). In that blog, I spent time addressing the many steps that are necessary for a predictive modeling project to be a success. Obviously, one of those critical steps is actual implementation of the model itself. Without that, you only have a fancy formula that doesn’t do much of anything for you. While insurers continue to devote more and more time and resources to predictive analytics, it would also benefit them to make sure they are devoting sufficient attention to model implementation.
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Commentary on NAIC’s Casualty Actuarial and Statistical Task Force White Paper – “Regulatory Review of Predictive Models”
Greg Frankowiak March 24, 2020 Posted in: Blog Posts, News, Predictive Analytics

While predictive analytics can provide significant benefits to insurance companies and customers, the rapid pace at which analytics is evolving and the relative complexity of some of the models used poses a significant challenge to state regulators who are charged with reviewing and approving such models. The National Association of Insurance Commissioners (NAIC) recognized this emerging issue and created the Casualty Actuarial and Statistical Task Force (CASTF), which has been charged with identifying best practices to guide state insurance departments in their review of predictive models for underlying rating plans. Over the course of the last year, the CASTF has released multiple drafts of the white paper “Regulatory Review of Predictive Models” for public comment. And comment the public has! Numerous letters have been submitted from trade associations, actuarial organizations, credit agencies, consumer groups and even insurance departments to provide their input on the lengthy white paper.

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