03846nam 2200925z- 450 991055766080332120231214133419.0(CKB)5400000000044898(oapen)https://directory.doabooks.org/handle/20.500.12854/68741(EXLCZ)99540000000004489820202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning in InsuranceBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 electronic resource (260 p.)3-03936-447-2 3-03936-448-0 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 and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.History of engineering & technologybicsscdeposit insuranceimplied volatilitystatic arbitrageparameterizationmachine learningcalibrationdichotomous responsepredictive modeltree boostingGLMvalidationgeneralised linear modellingzero-inflated poisson modeltelematicsbenchmarkcross-validationpredictionstock return volatilitylong-term forecastsoverlapping returnsautocorrelationchain ladderBornhuetter-Fergusonmaximum likelihoodexponential familiescanonical parametersprior knowledgeaccelerated failure time modelchain-ladder methodlocal linear kernel estimationnon-life reservingoperational timezero-inflationoverdispersionautomobile insurancerisk classificationrisk selectionleast-squares monte carlo methodproxy modelinglife insuranceSolvency IIclaims predictionexport credit insurancesemiparametric modelingVaR estimationanalyzing financial dataHistory of engineering & technologyNielsen Jens Perchedt1314788Asimit AlexandruedtKyriakou IoannisedtNielsen Jens PerchothAsimit AlexandruothKyriakou IoannisothBOOK9910557660803321Machine Learning in Insurance3031967UNINA