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norskBlar i forfatter Fakultet for naturvitenskap og teknologi "Aas, Kjersti"
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Deep generative models for reject inference in credit scoring
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-21)Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. Inspired by the promising results of semi-supervised deep generative models, this research develops two novel Bayesian models for reject inference in credit ... -
Discriminative multimodal learning via conditional priors in generative models
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-02)Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all ... -
Generating customer's credit behavior with deep generative models
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-17)Banks collect data x<sub>1</sub> in loan applications to decide whether to grant credit and accepted applications generate new data x<sub>2</sub> throughout the loan period. Hence, banks have two measurement-modalities, which provide a complete picture about customers. If we can generate x<sub>2</sub> conditioned on x<sub>1</sub> keeping the relationship between these two modalities, credit and ... -
Learning latent representations of bank customers with the Variational Autoencoder
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-15)Learning 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 ...
