Modeling & Statistical Methods
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Quantum Actuary：Reshaping Insurance Cognition
This article will mainly explore the quantum characteristics in some insurance phenomena. By using the quantum theory, this article will aim to revolutionize traditional insurance thinking, reshape the insurance cognition, and explore the new paths for modern actuarial science. -
Factorization Machines for High Cardinality Features (Part 4 of 4)
This is the fourth in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features. In the prior episodes we focused on approaches to handling individual high cardinality features, but these methods did not explicitly address feature interactions. Factorization Machines can responsibly estimate all pairwise interactions, even when multiple high cardinality features are included. With a healthy selection of high cardinality features, a well tuned Factorization Machine can produce results that are more accurate than any other learning algorithm. -
Meet Hybrid Data: A Blend of Alternative and Traditional Data. A Case Study to Construct an Improved Inflation Index
In the following article, I introduce the concept of “hybrid data,” a combination of alternative and traditional data, which I illustrate through an example on inflation, to be of better value than considering purely a traditional data or alternative data source alone. We present a case where we use alternative data from Zillow, to improve upon the Consumer Price Index (CPI), and thus create an index that is more pertinent to consumers and investors alike. -
Deep Learning in Segregated Fund Valuation: Part 2
This article is the second part of an article that appeared in April 2022 on the Emerging Topics Community webpage. It will discuss the data preparation, hyperparameter tuning and selection, and the training and testing process of the deep learning models. To reach the final conclusions, the article will continue to compare the projected cash flow results from LSTM and LSTM-Attn with those from the traditional method, and evaluate the time series generations of interest rates and equity returns by WGAN and TCN-GAN -
The Probability Principle of Group Testing: The Full-Scale Nucleic Acid Testing in Tianjin
On January 9 2022, a full-scale nucleic acid testing in Tianjin was launched. Over 10 millions of people were tested with the results announced within 2 days. The speedy efficiency was partly due to group testing with 10 persons per group. With this background, the aim of this article is to explain the probabilistic principle underlying group testing. To make the expository vivid, some numerical results and figures were provided using R language, a popular software in actuarial science and statistics. -
Anders vs. Shea, Part 4: A Champion is Crowned
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, reveal the results of the competition and share some final thoughts on the 2021 Milliman Health Practice Hackathon. -
Anders vs. Shea, Part 2: Anders’ Story
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, are joined by Nick Vander Heyden to discuss the approach used by Anders’ team in the 2021 Milliman Health Practice Hackathon. -
Emerging Topics Community: Anders vs. Shea, Part 1: Setting the Stage
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, are are joined by the organizers of the 2021 Milliman Health Practice Hackathon: Riley Heckel, FSA, MAAA, Austin Barrington, FSA, MAAA, and Phil Ellenberg. -
Using a Statistical Framework and AI Techniques to Enhance Basic Actuarial Assumptions Part 2: Application to Accelerated Underwriting
The paper uses the actuarial and statistical framework developed in Part 1 of the paper and discusses an application of this model in accelerated underwriting. It is a methodical way to quantify the impact of various risk factors in the underwriting process besides age and gender and provide an actuarially rigorous process to evaluate the aggregate risk level of a new policyholder. -
A Look Into Current Life Insurance Modeling Programs and Processes
Oliver Wyman recently completed a survey focused on actuarial hot topics and trends related to life insurance products with a specific focus on modeling and analysis programs. With over 40 companies participating, the survey results provide a broad industry perspective on modeling software preferences, runtime reduction techniques, and front-end and back-end process approaches. This article summarizes key insights with respect to participant modeling programs and processes.
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