Automated Machine Learning in Insurance

Machine Learning (ML), as described by Mitchell et al. (1990), is a multidisciplinary subfield of Artificial Intelligence (AI) focused on developing and implementing algorithms and statistical models that enable computer systems to perform data-driven tasks or make predictions through "leveraging data" and iterative learning processes. This data-driven approach guides the design of ML algorithms, allowing them to grasp the distributions and structures within datasets and unveil correlations that elude traditional mathematical and statistical methods. Professionals in data-related fields, such as data scientists and ML engineers, can engage in autonomous decision-making based on data and benefit from cutting-edge predictions generated by modern ML models. In recent decades, ML has significantly reshaped various industries and gained widespread popularity in academia due to its exceptional predictive capabilities. As summarized by Jordan and Mitchell (2015), ML has made significant contributions in various fields, including robotics, autonomous driving, language processing, and computer vision. The medical and healthcare industry, as suggested by Kononenko (2001) and Qayyum et al. (2020), is increasingly adopting ML for applications such as medical image analysis and clinical treatments. Furthermore, ML models have significantly improved personalization and targeting, marketing strategy, and customer engagement in the marketing sector, as summarized by Ma and Sun (2020). Guerra and Castelli (2021) present the ML innovations in the banking sector, particularly in the analysis of liquidity risks, bank risks, and credit risks. Additionally, there is a growing trend in adopting ML models in the insurance sector and among actuarial researchers and industry practitioners, as evidenced by recent literature.