LEADER 03846nam 2200925z- 450 001 9910557660803321 005 20231214133419.0 035 $a(CKB)5400000000044898 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68741 035 $a(EXLCZ)995400000000044898 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning in Insurance 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (260 p.) 311 $a3-03936-447-2 311 $a3-03936-448-0 330 $aMachine 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. 606 $aHistory of engineering & technology$2bicssc 610 $adeposit insurance 610 $aimplied volatility 610 $astatic arbitrage 610 $aparameterization 610 $amachine learning 610 $acalibration 610 $adichotomous response 610 $apredictive model 610 $atree boosting 610 $aGLM 610 $avalidation 610 $ageneralised linear modelling 610 $azero-inflated poisson model 610 $atelematics 610 $abenchmark 610 $across-validation 610 $aprediction 610 $astock return volatility 610 $along-term forecasts 610 $aoverlapping returns 610 $aautocorrelation 610 $achain ladder 610 $aBornhuetter-Ferguson 610 $amaximum likelihood 610 $aexponential families 610 $acanonical parameters 610 $aprior knowledge 610 $aaccelerated failure time model 610 $achain-ladder method 610 $alocal linear kernel estimation 610 $anon-life reserving 610 $aoperational time 610 $azero-inflation 610 $aoverdispersion 610 $aautomobile insurance 610 $arisk classification 610 $arisk selection 610 $aleast-squares monte carlo method 610 $aproxy modeling 610 $alife insurance 610 $aSolvency II 610 $aclaims prediction 610 $aexport credit insurance 610 $asemiparametric modeling 610 $aVaR estimation 610 $aanalyzing financial data 615 7$aHistory of engineering & technology 700 $aNielsen$b Jens Perch$4edt$01314788 702 $aAsimit$b Alexandru$4edt 702 $aKyriakou$b Ioannis$4edt 702 $aNielsen$b Jens Perch$4oth 702 $aAsimit$b Alexandru$4oth 702 $aKyriakou$b Ioannis$4oth 906 $aBOOK 912 $a9910557660803321 996 $aMachine Learning in Insurance$93031967 997 $aUNINA