Transaction time models in multi-state life insurance

Buchardt, Kristian; Furrer, Christian; Sandqvist, Oliver Lunding

doi:10.1080/03461238.2023.2181708

Cited by 1 publication

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Q1.2: Regarding the pricing of non-life products, the problems focus on model simplification through feature selection, data cleaning, and the extraction of outliers, along with techniques to improve prediction capacity, such as RNN and SHAP [25,26], isotonic recalibration [27], tree-based ensemble [28], Hierarchical Risk-factors Adaptive Top-down (PHiRAT) [29], logistic regression, decision tree, random forest, XGBoost, feed-forward network [30], transaction models for in life IBNR, inconclusive [31], integration of graphic themes [32], and extreme event estimation [33]. Moreover, currently, more efficient prediction models have been developed with techniques such as extreme gradient boosting or XGBoost [34], Bayesian CART models [35], boosting [36], and deep neural networks [36][37][38], among others.…”

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