This study proposes an efficacious approach to analyze the over-dispersed insurance frequency data as it is imperative … for the insurers to have decisive informative insights for precisely underwriting and pricing insurance products …, retaining existing customer base and gaining an edge in the highly competitive retail insurance market. The delta boosting …

The growing trend in the number and severity of auto insurance claims creates a needfor new methods to efficiently … study considers how automotive insurance providers incorporate machinery learning intheir company, and explores how ML … models can apply to insurance big data. We utilize various MLmethods, such as logistic regression, XGBoost, random forest …

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN …) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of … model specification and its implementation, model explainability may be essential to meet insurance pricing transparency for …

The growing trend in the number and severity of auto insurance claims creates a needfor new methods to efficiently … study considers how automotive insurance providers incorporate machinery learning intheir company, and explores how ML … models can apply to insurance big data. We utilize various MLmethods, such as logistic regression, XGBoost, random forest …

the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim … algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry. …