Rethinking Representations in P&C Actuarial Science with Deep Neural Networks Article Swipe
Christopher Blier-Wong
,
Jean-Thomas Baillargeon
,
Hélène Cossette
,
Luc Lamontagne
,
Étienne Marceau
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.05784
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.05784
Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many emerging data sources (text, images, sensors) may complement traditional data to provide better insights to predict the future losses in an insurance contract. This paper presents some of these emerging data sources and presents a unified framework for actuaries to incorporate these in existing ratemaking models. Our approach stems from representation learning, whose goal is to create representations of raw data. A useful representation will transform the original data into a dense vector space where the ultimate predictive task is simpler to model. Our paper presents methods to transform non-vectorial data into vectorial representations and provides examples for actuarial science.
Related Topics
Concepts
Complement (music)
Representation (politics)
Variety (cybernetics)
Computer science
Task (project management)
Process (computing)
External Data Representation
Raw data
Artificial intelligence
Artificial neural network
Space (punctuation)
Data science
Machine learning
Economics
Chemistry
Politics
Operating system
Programming language
Political science
Phenotype
Management
Gene
Complementation
Biochemistry
Law
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.05784
- https://arxiv.org/pdf/2102.05784
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394653800
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4394653800Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.05784Digital Object Identifier
- Title
-
Rethinking Representations in P&C Actuarial Science with Deep Neural NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-11Full publication date if available
- Authors
-
Christopher Blier-Wong, Jean-Thomas Baillargeon, Hélène Cossette, Luc Lamontagne, Étienne MarceauList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.05784Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2102.05784Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2102.05784Direct OA link when available
- Concepts
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Complement (music), Representation (politics), Variety (cybernetics), Computer science, Task (project management), Process (computing), External Data Representation, Raw data, Artificial intelligence, Artificial neural network, Space (punctuation), Data science, Machine learning, Economics, Chemistry, Politics, Operating system, Programming language, Political science, Phenotype, Management, Gene, Complementation, Biochemistry, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.space | 101 |
| abstract_inverted_index.stems | 76 |
| abstract_inverted_index.them. | 24 |
| abstract_inverted_index.these | 56, 69 |
| abstract_inverted_index.where | 102 |
| abstract_inverted_index.whose | 80 |
| abstract_inverted_index.(text, | 31 |
| abstract_inverted_index.better | 40 |
| abstract_inverted_index.create | 84 |
| abstract_inverted_index.future | 45 |
| abstract_inverted_index.gather | 2 |
| abstract_inverted_index.losses | 46 |
| abstract_inverted_index.model. | 110 |
| abstract_inverted_index.models | 18 |
| abstract_inverted_index.useful | 90 |
| abstract_inverted_index.vector | 100 |
| abstract_inverted_index.growing | 4 |
| abstract_inverted_index.images, | 32 |
| abstract_inverted_index.methods | 114 |
| abstract_inverted_index.models. | 73 |
| abstract_inverted_index.predict | 43 |
| abstract_inverted_index.provide | 39 |
| abstract_inverted_index.simpler | 108 |
| abstract_inverted_index.sources | 30, 59 |
| abstract_inverted_index.support | 23 |
| abstract_inverted_index.unified | 63 |
| abstract_inverted_index.variety | 5 |
| abstract_inverted_index.approach | 75 |
| abstract_inverted_index.designed | 21 |
| abstract_inverted_index.emerging | 28, 57 |
| abstract_inverted_index.examples | 124 |
| abstract_inverted_index.existing | 71 |
| abstract_inverted_index.insights | 41 |
| abstract_inverted_index.original | 95 |
| abstract_inverted_index.presents | 53, 61, 113 |
| abstract_inverted_index.process, | 13 |
| abstract_inverted_index.provides | 123 |
| abstract_inverted_index.science. | 127 |
| abstract_inverted_index.sensors) | 33 |
| abstract_inverted_index.ultimate | 104 |
| abstract_inverted_index.Insurance | 0 |
| abstract_inverted_index.actuarial | 126 |
| abstract_inverted_index.actuaries | 66 |
| abstract_inverted_index.companies | 1 |
| abstract_inverted_index.contract. | 50 |
| abstract_inverted_index.framework | 64 |
| abstract_inverted_index.insurance | 12, 49 |
| abstract_inverted_index.learning, | 79 |
| abstract_inverted_index.transform | 93, 116 |
| abstract_inverted_index.vectorial | 120 |
| abstract_inverted_index.complement | 35 |
| abstract_inverted_index.predictive | 105 |
| abstract_inverted_index.ratemaking | 17, 72 |
| abstract_inverted_index.incorporate | 68 |
| abstract_inverted_index.particular, | 26 |
| abstract_inverted_index.traditional | 16, 36 |
| abstract_inverted_index.non-vectorial | 117 |
| abstract_inverted_index.representation | 78, 91 |
| abstract_inverted_index.representations | 85, 121 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |