LEADER 02856nam 2200457z- 450 001 9910404090203321 005 20231214132850.0 010 $a3-03928-665-X 035 $a(CKB)4100000011302236 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/43322 035 $a(EXLCZ)994100000011302236 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClaim Models: Granular Forms and Machine Learning Forms 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (108 p.) 311 $a3-03928-664-1 330 $aThis collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications with real-world data. The examples include neural networks, which, though well known in some disciplines, have previously been limited in the actuarial literature. This volume expands on that literature, with specific attention to their application to loss reserving. For example, one of the articles introduces the application of neural networks of the gated recurrent unit form to the actuarial literature, whereas another uses a penalized neural network. Neural networks are not the only form of machine learning, and two other papers outline applications of gradient boosting and regression trees respectively. Both articles construct loss reserves at the individual claim level so that these models resemble granular models. One of these articles provides a practical application of the model to claim watching, the action of monitoring claim development and anticipating major features. Such watching can be used as an early warning system or for other administrative purposes. Overall, this volume is an extremely useful addition to the libraries of those working at the loss reserving frontier. 517 $aClaim Models 610 $agranular models 610 $aneural networks 610 $aactuarial 610 $apayments per claim incurred 610 $arisk pricing 610 $amachine learning 610 $aclaim watching 610 $aloss reserving 610 $agradient boosting 610 $apredictive modeling 610 $aclassification and regression trees 610 $aindividual models 610 $aindividual claims reserving 700 $aTaylor$b Greg$4auth$0617436 906 $aBOOK 912 $a9910404090203321 996 $aClaim Models: Granular Forms and Machine Learning Forms$93040130 997 $aUNINA