Daniel Lupton, FCAS, MAAA, CSPA, MBA

Daniel Lupton is a Certified Specialist in Predictive Analytics, Fellow of the Casualty Actuarial Society, a Member of the American Academy of Actuaries and holds a BA with Comprehensive honors in Mathematics and English from University of Wisconsin – Madison, and an MBA from the University of Maryland Robert H. Smith School of Business, with fifteen years’ actuarial experience in the property & casualty insurance actuarial industry.

At Taylor & Mulder Inc., Mr. Lupton has been responsible for reviewing rate filings on behalf of insurance regulators, analyzing insurance and self-insurance reserves, providing expert witness testimony, and providing actuarial services in support of risk-focused financial examinations of insurance companies.

Mr. Lupton has particular expertise with pollution liability, particularly underground and aboveground petroleum storage tank insurance and coal mine reclamation. Mr. Lupton’s experience also includes a wide variety of lines such as workers’ compensation, general liability, commercial auto liability, mortgage guaranty, medical professional liability, cyber liability, homeowners, inland marine, property, dwelling fire, farmowners, personal umbrella, motorcycle, private passenger automobile, equipment breakdown, environmental liability, crop insurance, surety, fiduciary insurance, aviation title insurance, libel and slander insurance, felonious assault, construction defect, dwelling fire, earthquake, wildfire, and flood insurance, among others.

Mr. Lupton has significant experience providing rate model review services. Mr. Lupton has managed the review of over 300 rate filings for state insurance departments since 2017 alone, and many more before that. In this context, Mr. Lupton has reviewed a large variety of models including Generalized Linear Models (GLMs), Ridge Regression, LASSO, and Elastic Net Regularization, Cubic Splines, Principal Components Analysis (PCA), (Bayesian) Hierarchical Models / Random Effects Models / Fixed Effects Models, Gradient Boosting Machines (GBMs), Generalized Additive Models (GAMs), Random Forests / Classification and Regression Trees (CART), ensembles, and various clustering techniques, including tree-based methods, k-means, and k-nearest-neighbors. In addition, Mr. Lupton, has reviewed catastrophe models including flood, wildfire, earthquake, and hurricane models, and he has led the review of numerous telematics and usage-based insurance models.

Mr. Lupton has pursued and obtained the newly developed designation of “Certified Specialist in Predictive Analytics” from the Casualty Actuarial Society. Mr. Lupton reviews and referees research papers on cutting-edge statistical methods for the CAS’s Variance journal as well as Computational Management Science. Mr. Lupton has served on numerous Casualty Actuarial Society Committees including as Chair of the CAS Machine Learning Working Party and as a Member of the CAS Ratemaking Committee, the Committee on Risk (previously called the “Dynamic Risk Modeling Committee”), the Membership Advisory Panel of the Casualty Actuarial Society, the Diversity Committee of the Casualty Actuarial Society, and the Editorial Review Staff of the Variance Actuarial Journal.

Mr. Lupton has also published the following papers:

Lupton, D. & Kuo, K. (in press). Towards Explainability of Machine Learning in Insurance Pricing. Variance.

Lupton, D., et al. Machine Learning in Insurance. CAS E-Forum.