Innovation & Technology | Machine learning

Prize-Winning Paper Tackles Machine Learning Actuarial Models

The actuarial profession shares in the potential power of ML, but new technologies can be disruptive Roel Henckaerts April 2023

The benefits of machine learning (ML) are widespread. It can help organizations get the most out of raw data, and its applications hold the promise of improving lives. The actuarial profession shares in the potential power of ML, but new technologies can be disruptive. ML actuarial models, for example, can be so complex they lack transparency, threatening to weaken consumers’ trust and raise regulators’ suspicions.

The timely topic of actuarial ML was the focus of a North American Actuarial Journal (NAAJ) article that was chosen as the best in 2021. This prize-winning paper looks inside ML pricing models to understand how they work and explore real-world applications.

The NAAJ’s Annual Prize

Each year since 1983, the NAAJ editorial board selects an article that stands out from the dozens published. The 2021 prize-winning article, “Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods,” authored by Roel Henckaerts, Marie-Pier Côté, Katrien Antonio and Roel Verbelen, looks at the inner workings of ML pricing models and develops use cases for pricing actuaries.

Disassembling the Black Box

Are there ways to use ML modeling to improve generalized linear models (GLMs)? Can methods be developed that make ML pricing algorithms understandable to stakeholders? These are a couple of the questions the 2021 NAAJ article of the year investigates.

The authors began collaborating when Marie-Pier Côté, assistant professor and educational leadership chair for big data analysis in actuarial science at Laval University, Quebec City, visited KU Leuven in Belgium for a research stay with Katrien Antonio, a professor in actuarial science and insurance analytics. Both Roel Verbelen and Roel Henckaerts were Antonio’s graduate students at the time. The four of them shared an interest in ML applications, and this brought them naturally to their paper’s topic.

“My team members and I wanted to write a paper that introduced ML concepts in combination with practical use-cases,” stated Henckaerts, now a senior data scientist at Prophecy Labs in Brussels.

The paper provides an overview of tree-based ML methods and provides a case study to show how to adopt ML for actuarial modeling. It follows up with an in-depth comparative study. The authors hope the paper helps actuaries understand how to extract insights from models and assess economic value when comparing it with their existing premium structure.

“The paper brings tools and technicalities together,” said Antonio. “It brings the full story: from the technical details relevant to tune the tree-based machine learning models, to the creation of a technical pricing model, the extraction of essential insights and a thorough comparison with the price list obtained with predictive models (like GLMs) that are well known to actuaries.”

The article’s authors also devised a way to use well-known techniques to explain a model’s logic. This is an important step in creating more transparency. When a model is transparent, consumers understand their risk assessment and resulting pricing structure, regulators can assess the fairness and affordability of premiums, and insurers can assess impact and employ better governance of models.

“Models carry a societal responsibility in terms of fairness and a right to explanation,” explained Verbelen, a senior consultant at Finity in Sydney.

Real-World Applications

The team has discovered that the techniques they demonstrate in their paper are applicable across insurance products. “The particular field matters less than the specific use case and the data available for it,” stated Verbelen.

In his job at a large consultancy, Verbelen regularly employs tree-based ML methods for general insurance applications, and he uses modeling interpretability tools to understand the models’ reasoning.

Additionally, Côté has seen many insurance companies in Canada use the gradient boosting techniques discussed in the paper as their preferred ML method for claim prediction. And Henckaerts, who has worked in various industries as a data consultant, applies the paper’s techniques in a range of uses, such as determining medical diagnoses, stock market returns, real-estate valuations, fraud detection and churn analysis, to name a few.

Explore Tree-Based ML Modeling

ML actuarial applications continue to grow and affect the profession, and it’s crucial to have techniques and tools that ensure models are working appropriately and transparently. “The most interesting part of being an actuary is thinking through whether models make sense and extrapolate well,” said Verbelen.

Learn more about actuarial tree-based ML models in the prize-winning NAAJ article: “Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods.”

Roel Henckaerts is a partner and the lead data scientist at Prophecy Labs, Brussels, a startup that generates business value from data for its clients. He received his Ph.D. from KU Leuven, Belgium, where he researched the interplay between insurance pricing, data science, machine learning and telematics, with a focus on interpretability and transparency.

Marie-Pier Côté, FSA, ACIA, holds an educational leadership chair in big data analysis for actuarial science. She is an assistant professor at the School of Actuarial Science at Université Laval. Professor Côté received a master’s degree and a Ph.D. in statistics from McGill University, Montreal. Her research interests include insurance risk dependence modeling and the development of statistical learning models for pricing and reserves in general insurance.

Katrien Antonio is a professor of actuarial science and insurance analytics at KU Leuven in Belgium and at the University of Amsterdam. She holds a Ph.D. in statistics and actuarial science from KU Leuven and MSc in mathematics, also from KU Leuven. Professor Antonio’s research topics include actuarial statistics, stochastic loss reserving, pricing, stochastic mortality modeling, loss models, insurance analytics and insurance data science.

Roel Verbelen is a senior consultant at Finity in Sydney, Australia. He received his Ph.D. in business statistics and actuarial science from KU Leuven, Belgium, where his research focused on the innovative use of statistics and data analytics in the general insurance sector.

Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.