Most Popular

Erool Palipane

How to Communicate the Benefits of Non-Traditional Modeling Techniques for Insurance Pricing

A look at new explanatory tools that better communicate the predictive and interpretative power of new analytics models.

How to Communicate the Benefits of Non-Traditional Modeling Techniques for Insurance Pricing
Actuaries build models to analyze historical loss frequency and severity data to properly determine the price of a risk.  Understanding how those models function helps an actuary understand when the model works as it should, and those occasions when it does not. 
Tags:
New Approaches, Old Method: Predicting IBNR with Machine Learning
Multiple September 16, 2021 Posted in: Blog Posts, 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.
Tags:
1234
«September 2022»
SunMonTueWedThuFriSat
28293031123
45678910
11121314151617
18192021222324
2526272829301
2345678