Machine Learning in Insurance

• Other Authors: Nielsen, Jens Perch (Editor), Asimit, Alexandru (Editor), Kyriakou, Ioannis (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: History of engineering & technology; deposit insurance; implied volatility; static arbitrage; parameterization; machine learning; calibration; dichotomous response; predictive model; tree boosting; GLM; validation; generalised linear modelling; zero-inflated poisson model; telematics; benchmark; cross-validation; prediction; stock return volatility; long-term forecasts; overlapping returns; autocorrelation; chain ladder; Bornhuetter-Ferguson; maximum likelihood; exponential families; canonical parameters; prior knowledge; accelerated failure time model; chain-ladder method; local linear kernel estimation; non-life reserving; operational time; zero-inflation; overdispersion; automobile insurance; risk classification; risk selection; least-squares monte carlo method; proxy modeling; life insurance; Solvency II; claims prediction; export credit insurance; semiparametric modeling; VaR estimation; analyzing financial data; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand...