ORCID Profile
0000-0002-9488-2993
Current Organisation
University of Southampton
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Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 09-2020
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 16-05-2022
DOI: 10.1137/21M1422318
Publisher: Elsevier BV
Date: 02-2018
Publisher: Elsevier BV
Date: 02-2021
Publisher: Wiley
Date: 31-07-2020
DOI: 10.1002/PATH.5506
Publisher: Wiley
Date: 26-07-2021
DOI: 10.1111/JORI.12359
Abstract: The objective of this paper is to propose a novel deep learning methodology to gain pragmatic insights into the behavior of an insured person using unsupervised variable importance. It lays the groundwork for understanding how insights can be gained into the fraudulent behavior of an insured person with minimum effort. Starting with a preliminary investigation of the limitations of the existing fraud detection models, we propose a new variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational autoencoder. Each model's dynamics is discussed to inform the reader on how models can be adapted for fraud detection and how results can be perceived appropriately. Both qualitative and quantitative performance evaluations are conducted, although a greater emphasis is placed on qualitative evaluation. To broaden the scope of reference of fraud detection setting, various metrics are used in the qualitative evaluation.
Publisher: MDPI AG
Date: 04-05-2018
DOI: 10.3390/RISKS6020051
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 12-2020
Publisher: Informa UK Limited
Date: 23-01-2019
Publisher: Elsevier BV
Date: 11-2016
Publisher: Elsevier BV
Date: 09-2015
Publisher: Informa UK Limited
Date: 22-02-2021
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 10-2022
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 05-2015
Publisher: Elsevier BV
Date: 11-2018
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Zhuo Jin.