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Article: Assessing ChatGPT's proficiency in quantitative risk management

TitleAssessing ChatGPT's proficiency in quantitative risk management
Authors
KeywordsChatGPT
dependence
extremes
quantitative risk management
risk
risk measures
time series
Issue Date19-Sep-2023
PublisherMDPI
Citation
Risks, 2023, v. 11, n. 9 How to Cite?
Abstract

We engage in a scholarly discussion with artificial intelligence chatbot ChatGPT on concepts in quantitative risk management important for actuarial practice and analyze the interaction. We investigate the extent to which ChatGPT can grasp concepts from the realm of risk, risk measures, time series, extremes and dependence. Non-technical aspects of risk (such as explanations of various types of financial risk, the driving factors underlying the financial crisis of 2007 to 2009, or a basic introduction to the Basel Framework) are well grasped. However, more technical aspects (such as mathematical facts) are often inaccurate or wrong, sometimes in rather subtle ways. We provide guidance on the types of applications for which consulting ChatGPT can be useful in order to enhance your knowledge about quantitative risk management in actuarial practice, and point out those for which ChatGPT should better not be invoked.


Persistent Identifierhttp://hdl.handle.net/10722/337080
ISSN
2020 SCImago Journal Rankings: 0.403

 

DC FieldValueLanguage
dc.contributor.authorHofert, Jan Marius-
dc.date.accessioned2024-03-11T10:17:56Z-
dc.date.available2024-03-11T10:17:56Z-
dc.date.issued2023-09-19-
dc.identifier.citationRisks, 2023, v. 11, n. 9-
dc.identifier.issn2227-9091-
dc.identifier.urihttp://hdl.handle.net/10722/337080-
dc.description.abstract<p>We engage in a scholarly discussion with artificial intelligence chatbot ChatGPT on concepts in quantitative risk management important for actuarial practice and analyze the interaction. We investigate the extent to which ChatGPT can grasp concepts from the realm of risk, risk measures, time series, extremes and dependence. Non-technical aspects of risk (such as explanations of various types of financial risk, the driving factors underlying the financial crisis of 2007 to 2009, or a basic introduction to the Basel Framework) are well grasped. However, more technical aspects (such as mathematical facts) are often inaccurate or wrong, sometimes in rather subtle ways. We provide guidance on the types of applications for which consulting ChatGPT can be useful in order to enhance your knowledge about quantitative risk management in actuarial practice, and point out those for which ChatGPT should better not be invoked.<br></p>-
dc.languageeng-
dc.publisherMDPI-
dc.relation.ispartofRisks-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectChatGPT-
dc.subjectdependence-
dc.subjectextremes-
dc.subjectquantitative risk management-
dc.subjectrisk-
dc.subjectrisk measures-
dc.subjecttime series-
dc.titleAssessing ChatGPT's proficiency in quantitative risk management-
dc.typeArticle-
dc.identifier.doi10.3390/risks11090166-
dc.identifier.scopuseid_2-s2.0-85172263185-
dc.identifier.volume11-
dc.identifier.issue9-
dc.identifier.eissn2227-9091-
dc.identifier.issnl2227-9091-

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