Machine Learning in Insurance
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...
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| Natura: | Online |
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| Lingua: | inglese |
| Pubblicazione: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Soggetti: | |
| Accesso online: | ONIX_20210501_9783039364473_487 |
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| _version_ | 1869522578717016064 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-68741 |
| 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-687412024-04-11T15:10:19Z Machine Learning in Insurance Nielsen, Jens Perch Asimit, Alexandru Kyriakou, Ioannis 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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. 2021-05-01T15:27:58Z 2021-05-01T15:27:58Z 2020 book ONIX_20210501_9783039364473_487 9783039364473 9783039364480 https://directory.doabooks.org/handle/20.500.12854/68741 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/2507 https://mdpi.com/books/pdfview/book/2507 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03936-448-0 10.3390/books978-3-03936-448-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039364473 9783039364480 260 Basel, Switzerland open access |
| spellingShingle | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Machine Learning in Insurance |
| title | Machine Learning in Insurance |
| title_full | Machine Learning in Insurance |
| title_fullStr | Machine Learning in Insurance |
| title_full_unstemmed | Machine Learning in Insurance |
| title_short | Machine Learning in Insurance |
| title_sort | machine learning in insurance |
| topic | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20210501_9783039364473_487 |