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dc.contributor.authorAndrade Mancisidor, Rogelio
dc.contributor.authorKampffmeyer, Michael
dc.contributor.authorAas, Kjersti
dc.contributor.authorJenssen, Robert
dc.date.accessioned2023-06-27T07:21:47Z
dc.date.available2023-06-27T07:21:47Z
dc.date.issued2020-09-15
dc.description.abstractLearning data representations that reflect the customers’ creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we show that it is possible to steer data representations in the latent space of the Variational Autoencoder (VAE) using a semi-supervised learning framework and a specific grouping of the input data called Weight of Evidence (WoE). Our proposed method learns a latent representation of the data showing a well-defied clustering structure. The clustering structure captures the customers’ creditworthiness, which is unknown a priori and cannot be identified in the input space. The main advantages of our proposed method are that it captures the natural clustering of the data, suggests the number of clusters, captures the spatial coherence of customers’ creditworthiness, generates data representations of unseen customers and assign them to one of the existing clusters. Our empirical results, based on real data sets reflecting different market and economic conditions, show that none of the well-known data representation models in the benchmark analysis are able to obtain well-defined clustering structures like our proposed method. Further, we show how banks can use our proposed methodology to improve marketing campaigns and credit risk assessment.en_US
dc.identifier.citationAndrade Mancisidor, Kampffmeyer, Aas, Jenssen. Learning latent representations of bank customers with the Variational Autoencoder. Expert systems with applications. 2020;164en_US
dc.identifier.cristinIDFRIDAID 1832141
dc.identifier.doi10.1016/j.eswa.2020.114020
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.urihttps://hdl.handle.net/10037/29502
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalExpert systems with applications
dc.relation.projectIDNorges forskningsråd: 260205en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleLearning latent representations of bank customers with the Variational Autoencoderen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)