Claim Models: Granular Forms and Machine Learning Forms
This 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...
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| Format: | Online |
| Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | 46002 |
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| _version_ | 1869519840183582720 |
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| author | Taylor, Greg |
| author_browse | Taylor, Greg |
| author_facet | Taylor, Greg |
| author_sort | Taylor, Greg |
| collection | Directory of Open Access Books |
| description | This 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. |
| format | Online |
| id | doab-20.500.12854ir-43322 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-433222024-04-11T15:10:56Z Claim Models: Granular Forms and Machine Learning Forms Taylor, Greg TJ1-1570 TA1-2040 T1-995 n/a granular models neural networks actuarial payments per claim incurred risk pricing machine learning claim watching loss reserving gradient boosting predictive modeling classification and regression trees individual models individual claims reserving thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TD Industrial chemistry and manufacturing technologies::TDC Industrial chemistry and chemical engineering::TDCW Pharmaceutical chemistry and technology This 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. 2021-02-11T09:58:11Z 2021-02-11T09:58:11Z 2020-06-09 16:38:57 2020 book 46002 9783039286645 9783039286652 https://directory.doabooks.org/handle/20.500.12854/43322 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/2177 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-665-2 10.3390/books978-3-03928-665-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039286645 9783039286652 108 open access |
| spellingShingle | TJ1-1570 TA1-2040 T1-995 n/a granular models neural networks actuarial payments per claim incurred risk pricing machine learning claim watching loss reserving gradient boosting predictive modeling classification and regression trees individual models individual claims reserving thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TD Industrial chemistry and manufacturing technologies::TDC Industrial chemistry and chemical engineering::TDCW Pharmaceutical chemistry and technology Taylor, Greg Claim Models: Granular Forms and Machine Learning Forms |
| title | Claim Models: Granular Forms and Machine Learning Forms |
| title_full | Claim Models: Granular Forms and Machine Learning Forms |
| title_fullStr | Claim Models: Granular Forms and Machine Learning Forms |
| title_full_unstemmed | Claim Models: Granular Forms and Machine Learning Forms |
| title_short | Claim Models: Granular Forms and Machine Learning Forms |
| title_sort | claim models granular forms and machine learning forms |
| topic | TJ1-1570 TA1-2040 T1-995 n/a granular models neural networks actuarial payments per claim incurred risk pricing machine learning claim watching loss reserving gradient boosting predictive modeling classification and regression trees individual models individual claims reserving thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TD Industrial chemistry and manufacturing technologies::TDC Industrial chemistry and chemical engineering::TDCW Pharmaceutical chemistry and technology |
| topic_facet | TJ1-1570 TA1-2040 T1-995 n/a granular models neural networks actuarial payments per claim incurred risk pricing machine learning claim watching loss reserving gradient boosting predictive modeling classification and regression trees individual models individual claims reserving thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TD Industrial chemistry and manufacturing technologies::TDC Industrial chemistry and chemical engineering::TDCW Pharmaceutical chemistry and technology |
| url | 46002 |
| work_keys_str_mv | AT taylorgreg claimmodelsgranularformsandmachinelearningforms |