Customer price sensitivities in competitive insurance markets

Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

“…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.…”

“…On the one hand, it is essential to emphasize the relevance of transparent regulatory frameworks for artificial intelligence, differentiating between the explicability requirements of AI models themselves and the broader explicability obligations of AI systems under existing laws and regulations [43]. On the other hand, there are some challenges to address: for example, the definition of appropriate assessment methods for the banking sector, especially for fraud detection [34,35], and the interpretation of complex models like deep learning for various applications, including equity analysis and financial distress prediction [44][45][46][47][48]. Other challenges include reducing biases in research judgments [40] and emphasizing the role of cybersecurity in maintaining the integrity of AI systems [49].…”

\">i<When theres no November rain>/i<\": developing a parametric insurance for hydroelectric energy generators in Brazil

“…Other aspects of pricing include mechanisms of risk reduction (e.g., statistical loading, deductibles, maximum indemnity limits and reinsurance contracts), loading rates (to compensate commercial and administrative expenses, besides taxes and profit margin), commercial decisions (changes in prices due to market competitiveness) and utility matters (Guelman et al, 2014;Kaas et al, 2008;Laas et al, 2016;Mourdoukoutas et al, 2021;Omerašević & Selimović, 2020;Verschuren, 2022).…”