New Machine Learning Study Findings Reported from Youngstown State University (Machine Learning and Frequency-Severity Decomposition for Insurance Pricing): Machine Learning

2026 JUN 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Current study results on artificial intelligence have been published. According to news reporting out of Youngstown, Ohio, by NewsRx editors, research stated, “Insurance pricing plays a central role in risk management and financial decision-making, as accurate premium estimation directly impacts portfolio stability and profitability. This study investigates insurance pure premium estimation by integrating classical actuarial models with modern machine learning techniques.”

Our news journalists obtained a quote from the research from Youngstown State University: “We compare the traditional frequency-severity decomposition framework with direct modeling approaches, including XGBoost and Tweedie models. For claim frequency, we evaluate Poisson-based models, generalized additive models, and XGBoost. For claim severity, we compare a Gamma generalized linear model with XGBoost. The results show that XGBoost improves predictive performance for both components based on the evaluation metrics considered. Within the decomposition framework, the XGBoost-XGBoost model achieves the lowest prediction error among the models considered. However, lift-based analysis reveals that the XGBoost-Gamma model provides superior risk segmentation, highlighting a trade-off between prediction accuracy and risk ranking. Direct modeling approaches, while competitive, do not consistently achieve lower error than the decomposition framework across the evaluation metrics considered. Overall, the findings demonstrate that machine learning enhances predictive performance, but its effectiveness is maximized within the frequency-severity framework.”

According to the news reporters, the research concluded: “The results highlight the importance of both frequency and severity modeling in insurance pricing, while suggesting that their relative contributions to risk segmentation depend on model specification and evaluation criteria. These findings have important implications for risk management and pricing strategies in insurance portfolios.”

For more information on this research see: Machine Learning and Frequency-Severity Decomposition for Insurance Pricing. Mathematics, 2026,14(10):1640. (Mathematics - http://www.mdpi.com/journal/mathematics). The publisher for Mathematics is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/math14101640.

Our news editors report that additional information may be obtained by contacting Nguyet Nguyen, Department of Mathematics and Statistics, Youngstown State University, Youngstown, OH 44555, United States.

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