Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data

Li, Hongjie; Luo, Xinggang; Zhang, Zhong-Liang; Jiang, Wei; Huang, Shen-Wei

doi:10.1016/j.dss.2023.113985

Cited by 5 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Q1.1: Problems related to rate balancing, classification for group rate generation, and prediction are faced. The central axis includes the following models: GLM [17], adaptive [18], adaptive Gaussian models [18], logistic regression [19], auto-encoder LSTM [20], neural networks [21], boosting [22], SMuRF [19], TabNet DL [23], XGBoost [22], as well as the implementation of protocols such as the sum-product network (SPN) [24] or the development of processes for data pre-processing.…”

Section: Results and Findingsmentioning

confidence: 99%

Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Lozano-Murcia,

Romero,

Serrano-Guerrero

et al. 2024

Mathematics

View full text Add to dashboard Cite

Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.

show abstract