Assessment of Creditworthiness Models Privacy-Preserving Training with Synthetic Data

Muñoz-Cancino, Ricardo; Bravo, Cristián; Ríos, Sebastián A.; Graña, Manuel

doi:10.1007/978-3-031-15471-3_32

Cited by 3 publications

(4 citation statements)

References 22 publications

(13 reference statements)

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“…However, there are some concerns regarding the application of such models. Considering that our results are consistent with previous studies [38,39], the GAN-based synthesizers are expected to require longer computation durations than the TVAE across almost all epochs. Meanwhile, the TVAE generated no variance data for several variables during the shorter epochs (5-30 epochs).…”

Section: Discussionsupporting

confidence: 92%

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

Watanuki,

Nomura,

Kiyota

et al. 2023

Applied Sciences

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Although a multimodal data analysis, comprising physiological and questionnaire survey data, provides better insights into addressing management science concerns, such as challenging the predictions of consumer choice behavior, studies in this field are scarce because of two obstacles: limited sample size and information privacy. This study addresses these challenges by synthesizing multimodal data using deep generative models. We obtained multimodal data by conducting an electroencephalography (EEG) experiment and a questionnaire survey on the prediction of skilled nurses. Subsequently, we validated the effectiveness of the synthesized data compared with real data regarding the similarities between these data and the predictive performance. We confirmed that the synthesized big data were almost equal to the real data using the trained models through sufficient epochs. Conclusively, we demonstrated that synthesizing data using deep generative models might overcome two significant concerns regarding multimodal data utilization, including physiological data. Our approach can contribute to the prevailing combined big data from different modalities, such as physiological and questionnaire survey data, when solving management issues.

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Section: Discussionsupporting

confidence: 92%

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

Watanuki,

Nomura,

Kiyota

et al. 2023

Applied Sciences

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“…Synthetic data can be used instead of real data to training models. Muñoz-Cancino and others (Muñoz-Cancino et al, 2022) made an attempt to evaluate creditworthiness assessment models based on realworld data and synthetic data generated by CTGAN and TVAE. The authors concluded that, despite results that were not fully satisfactory, by protecting the data privacy, such an action could boost cooperation between financial institutions and academia.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Autoencoders are unsupervised learning methods that are used in particular to deal with two analytical problems: dimensionality reduction and synthetic data (Muñoz-Cancino et al, 2022). Autoencoders are composed of two parts, an encoder and a decoder.…”

Section: Tvaementioning

confidence: 99%

“…Compared to classical autoencoders, variational autoencoders attempt to map input data into a multivariate Gaussian distribution in the latent space, and not to a vector, using an encoder (Figueira & Vaz, 2022;Podolszańska, 2021). The Tabular Variational Autoencoder (TVAE) is a type of variational autoencoders, adapted to generate tabular data (Xu et al, 2019), by using the evidence lower bound (ELBO) loss (Muñoz-Cancino et al, 2022).…”

Section: Tvaementioning

confidence: 99%

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Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data

Szymura

2024

Econometrics

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Aim: The aim of this article was to present and evaluate the concept of synthetic data. They are completely new, artificially generated data, but keep the statistical properties of real data. Due to the statistical similarity with real data, they can be used instead of them. This action allows data to be shared externally while guaranteeing their privacy. Methodology: New datasets were generated based on financial information about Polish limited liability companies, which come from the Orbis database and refer to 2020. To create synthetic data, it was decided to use generative models: CTGAN (based on GAN architecture) and TVAE (based on autoencoders). Lastly, the synthetic data were compared with the real ones in terms of statistical properties (e.g. shape of distributions, correlations etc.) and their applicability in data analysis (the PCA method). Results: The Overall Quality Score was higher for the data generated by TVAE, but after examining the results in more detail, it was seen that the data generated by CTGAN had a better quality in terms of keeping the statistical properties of the real data. Comparing the results of the PCA method, TVAE was better than CTGAN. In addition, the TVAE method was less time-consuming than CTGAN. Implications and recommendations: Before publishing the synthetic data externally, it is recommended that the data are generated using several algorithms, evaluating their final results and finally selecting the best option. This action enables the resulting dataset to be of the highest quality. In further research, it is proposed that other algorithms are tested (e.g. CopulaGAN or TableGAN), in an attempt to deal with some of the realistic data problems that were missed in this analysis, such as missing values (the work was carried out with a complete dataset). Data generated in this study may be used to build financial indicators, which in turn could be used to construct company assessment models. Originality/value: Synthetic data help to deal with some of the data limitations, such as data privacy or scarcity. Due to their statistical similarity with real data, it is possible to use them in advanced machine learning methods instead of real datasets. Analysis on high quality synthetic data allows conclusions similar to analysis on real data to be achieved, while retaining privacy and without publishing sensitive data to third parties.

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Applying Deep Generative Neural Networks to Data Augmentation for Consumer Survey Data with a Small Sample Size

Watanuki,

Edo,

Miura

2024

Applied Sciences

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Questionnaire consumer survey research is primarily used for marketing research. To obtain credible results, collecting responses from numerous participants is necessary. However, two crucial challenges prevent marketers from conducting large-sample size surveys. The first is cost, as organizations with limited marketing budgets struggle to gather sufficient data. The second involves rare population groups, where it is difficult to obtain representative samples. Furthermore, the increasing awareness of privacy and security concerns has made it challenging to ask sensitive and personal questions, further complicating respondent recruitment. To address these challenges, we augmented small-sized datawith synthesized data generated using deep generative neural networks (DGNNs). The synthesized data from three types of DGNNs (CTGAN, TVAE, and CopulaGAN) were based on seed data. For validation, 11 datasets were prepared: real data (original and seed), synthesized data (CTGAN, TVAE, and CopulaGAN), and augmented data (original + CTGAN, original + TVAE, original + CopulaGAN, seed + CTGAN, seed + TVAE, and seed + CopulaGAN). The large-sample-sized data, termed “original data”, served as the benchmark, whereas the small-sample-sized data acted as the foundation for synthesizing additional data. These datasets were evaluated using machine learning algorithms, particularly focusing on classification tasks. Conclusively, augmenting and synthesizing consumer survey data have shown potential in enhancing predictive performance, irrespective of the dataset’s size. Nonetheless, the challenge remains to minimize discrepancies between the original data and other datasets concerning the values and orders of feature importance. Although the efficacy of all three approaches should be improved in future work, CopulaGAN more accurately grasps the dependencies between the variables in table data compared with the other two DGNNs. The results provide cues for augmenting data with dependencies between variables in various fields.

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Assessment of Creditworthiness Models Privacy-Preserving Training with Synthetic Data

Cited by 3 publications

References 22 publications

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data

Applying Deep Generative Neural Networks to Data Augmentation for Consumer Survey Data with a Small Sample Size

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