DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Insu | ko |
dc.contributor.author | Koh, Woosung | ko |
dc.contributor.author | Koo, Bonwoo | ko |
dc.contributor.author | Kim, Woo Chang | ko |
dc.date.accessioned | 2024-01-16T07:00:14Z | - |
dc.date.available | 2024-01-16T07:00:14Z | - |
dc.date.created | 2024-01-16 | - |
dc.date.issued | 2024-02 | - |
dc.identifier.citation | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.128 | - |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.uri | http://hdl.handle.net/10203/317855 | - |
dc.description.abstract | Effective customer segmentation and communication of these findings to non-experts is a pressing task in the financial services sector, with the potential for widespread applications. This study employs a three-stage dimension reduction and clustering technique to segment a large, high-dimensional dataset, emphasizing explainability and intuitive visualization. We present the high-dimensional data and feature set using novel network-based visualization methods and identify the multi-stage process's optimal configuration. The approach segments 14,837 potential customers, each with 163 categorical and 143 numerical features. The first stage of the dimension reduction process employs deep neural network-based autoencoders. The second and third stage uses a non-neural network-based dimension reduction algorithm and clustering algorithm contingent on clus-tering performance. Subsequently, game theory-inspired Shapley values are computed for each feature to enhance explainability. The optimal approach involves an autoencoder, isometric mapping to three dimensions, and K-means clustering. Lastly, we derive investment portfolios for each segment to demonstrate an expert system application in financial investment advisory to underscore the importance of explainable segmentations. | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Network-based exploratory data analysis and explainable three-stage deep clustering for financial customer profiling | - |
dc.type | Article | - |
dc.identifier.wosid | 001132950800001 | - |
dc.identifier.scopusid | 2-s2.0-85178101693 | - |
dc.type.rims | ART | - |
dc.citation.volume | 128 | - |
dc.citation.publicationname | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | - |
dc.identifier.doi | 10.1016/j.engappai.2023.107378 | - |
dc.contributor.localauthor | Kim, Woo Chang | - |
dc.contributor.nonIdAuthor | Koh, Woosung | - |
dc.contributor.nonIdAuthor | Koo, Bonwoo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Network science | - |
dc.subject.keywordAuthor | Autoencoders | - |
dc.subject.keywordAuthor | Deep clustering | - |
dc.subject.keywordAuthor | Dimension reduction | - |
dc.subject.keywordAuthor | Explainable AI | - |
dc.subject.keywordAuthor | Financial expert system | - |
dc.subject.keywordPlus | DIMENSIONALITY REDUCTION | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | KNOWLEDGE | - |
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