Generative Adversarial Networks in Business and Social Science

Abstract

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics.

Full text

Citation: Ruiz-Gándara, A.;
Gonzalez-Abril, L. Generative
Adversarial Networks in Business and
Social Science. Appl. Sci. 2024,14,
7438. https://doi.org/10.3390/
app14177438
Academic Editors: Adnane Cabani
and Kévin Bouchard
Received: 24 July 2024
Revised: 16 August 2024
Accepted: 20 August 2024
Published: 23 August 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Review
Generative Adversarial Networks in Business and Social Science
Africa Ruiz-Gándara and Luis Gonzalez-Abril *
Department Applied Economic I, Faculty of Sciences of Economics and Business, University of Seville,
Avda. Ramón y Cajal, 1, E-41018 Sevilla, Spain; africaruiz@us.es
*Correspondence: luisgon@us.es; Tel.: +34-954-557539
Featured Application: The importance of generative adversarial networks (GANs) in economics is
growing and is driven by successes in other fields. Many economic problems could benefit from
GANs, although few studies exist and progress is needed. This paper argues for the use of GANs
as a novel and effective tool in economics. An important issue is the need for large datasets, where
traditional techniques fall short due to a multiplicity of problems, making the use of GANs very
useful in this task.
Abstract: Generative adversarial networks (GANs) have become a recent and rapidly developing
research topic in machine learning. Since their inception in 2014, a significant number of variants
have been proposed to address various topics across many fields, and they have particularly excelled
not only in image and language processing but also in the medical and data science domains. In
this paper, we aim to highlight the significance of and advancements that these GAN models can
introduce in the field of Business Economics, where they have yet to be fully developed. To this end,
a review of the literature of GANs is presented in general together with a more specific review in
the field of Business Economics, for which only a few papers can be found. Furthermore, the most
relevant papers are analysed in order to provide approaches for the opportunity to research GANs in
the field of Business Economics.
Keywords: GANs; multidisciplinary application; business economics; artificial intelligence; machine
learning
1. Introduction
Currently, we are facing a continuous technological advance that necessitates signifi-
cant societal changes. In light of this new social structure, massive data holds particular
interest, since access to this resource signifies major privileges. Traders, manufacturers,
suppliers, insurers, and entrepreneurs all require access to and the ability to utilise ex-
tensive databases that enable them to study and uncover the necessary information to
establish commercial strategies. This access also allows them to understand customer pro-
files and preferences, thereby enabling informed decision-making on pricing, promotions,
risk assessment, competition, business models, and more. However, these highly cov-
eted volumes of data have surpassed human capacity for collection, storage, and analysis.
Consequently, to undertake these tasks effectively, humans require suitable tools, such as
statistics intelligence and machine learning.
Major advances are being made in machine learning, which has been utilised to
recognise patterns and identify objects. However, recently there has been a leap towards
generating entirely new objects and individuals. Generative models are capable of cre-
ating all kinds of content from images and text. Among them, we highlight: generative
adversarial networks (GANs), transformers, variational autoencoders (VAEs), diffusion
models, and neural radiance fields (NeRFs). It is worth noting that when choosing one
model or another, one should consider choosing a model that works well for the problem
to be tackled. Thus, for example, one would use transformers for translation tasks, NeRFs
Appl. Sci. 2024,14, 7438. https://doi.org/10.3390/app14177438 https://www.mdpi.com/journal/applsci

Appl. Sci. 2024,14, 7438 2 of 23
for 3D scenes, and GANs for the generation of synthetic datasets. It is important that a
sufficient quantity and quality of data be available; thus, for example, transformers need a
large volume of data, but GANs work better with less data. Another aspect to consider is
the quality of the results: GANs are better at obtaining clear and detailed images, while
VAE obtains more uniform results.
This paper is focused on GANs. Thus, by using two antagonistic neural networks,
ref. [
1
] proposes a tool called “Generative Adversarial Networks” (GANs), which are
capable of creating faces of people that are not real, and it is difficult to differentiate these
from real faces. While this is one of the most popular applications of GANs, their true
importance lies in their capability to generate synthetic data that appear indistinguishable
from real data.
In this paper, a bibliographic review of GANs is conducted that focuses on contri-
butions made in the areas of Business and Social Science. Presently, within the literature,
various reviews on GANs in general can be found [
2
–
7
]. However, none of these specifically
concentrates on the potential applications within the field of Business Economics. The
objective is to showcase the potential of this tool within these two areas.
The rest of this paper is structured as follows: Section 2briefly defines a GAN and
how it functions. Section 3presents a statistical study of publications on GANs gathered
from the Web of Science. In Section 4, the focus is placed on GAN publications within the
scope of this work: Business Economics. Section 5details the relevance of this tool in the
field of Business Economics. Section 6outlines the conclusions drawn.
2. Generative Adversarial Networks
The main aim of GANs is the automatic generation of data. Its main difference
from other generative models is that it does not directly use the distribution of real data;
instead, it operates through a classifier. The generative model is random and adjusts itself
step-by-step based on the classifier’s response, continually refining the output until the
discriminator is unable to differentiate between real and synthetic data.
In order to achieve this objective, two neural networks—namely, the generator and
the discriminator—are employed, which engage in a competitive process. The genera-
tor produces synthetic data resembling real data, while the discriminator endeavours to
distinguish between real data and the data supplied by the generator.
This competition between the generator and discriminator can be viewed through
game theory as a zero-sum game since the two networks have conflicting objectives; as one
network improves, the other deteriorates. Consequently, the minimax algorithm can be
employed, which is a decision-making algorithm that minimises the maximum loss in the
game [
8
]. Thus, the training objective of GANs is to find the Nash equilibrium. Ref. [
1
]
demonstrated that the Nash equilibrium is reached when the generator produces samples
indistinguishable from the training dataset and the discriminator can only randomly guess
whether a sample is real or fake, which means the generated samples are indistinguishable
to the discriminator. When the Nash equilibrium is achieved, it is said that the network
converges. In practice, GAN training is conducted using techniques that are not explicitly
designed to find the Nash equilibrium, which may potentially lead to non-convergence [
9
].
At times, achieving convergence might necessitate the imposition of stringent conditions,
which can be challenging to meet in practice [10–12].
The goal of the generator is to fool the discriminator, and the goal of the discriminator
is to not be fooled. This confrontation leads to the generator being increasingly capable of
providing synthetic data that is more similar to real data. The ideal solution in the GAN
model is that the percentage of success of the discriminator for the real data and synthetic
data is 50% for both cases. The structure of a GAN model for a database can be observed in
Figure 1.

Appl. Sci. 2024,14, 7438 3 of 23
Figure 1. Structure of a generative adversarial network model [13].
2.1. Mathematical Framework
Let us give a technical introduction of the GAN model: Given a database with
m
real samples (
x
) (training data) and a random noise vector (
z
), the following terms are
considered:
•G(z)is the output of the generator from the noise z: that is, it is the synthetic data.
•D(x)is the output of the discriminator when a real sample xis processed.
•D(G(z)) is the prediction of the discriminator on the synthetic data.
•Pxand Pzare the distributions of real and noise data, respectively.
•Ex
and
EG(z)
are the expected log likelihoods from the different outputs of real and
generated data, respectively.
•θDand θGare the weights of the discriminator and generator model, respectively.
The expression to be considered for the complete network comprising the discriminator
and generator is denoted by Vand is as follows:
V(θD,θG) = Ex∼Px[log D(x)] + Ez∼Pz[log(1−D(G(z)))]. (1)
This value function is submitted to a min–max strategy with the goal of maximising
the discriminator loss and minimising the generator loss:
min
θGmax
θDV(θD,θG). (2)
Furthermore, a suitable setup for each artificial neural network employed and its
training is required for the implementation of a GAN.
2.2. Variants of GANs
The training of GANs presents some major challenges: for example, mode collapse [
14
],
non-convergence [
11
,
15
,
16
], and instability [
17
], among others [
18
,
19
]. Furthermore, a GAN
can be structured to address a specific problem. Thus, some variants of GANs have
been proposed in order to overcome these challenges by redesigning the generator and
discriminator in the network architecture, changing the form of loss functions, and altering
optimization algorithms. Hence, the following variants of GANs are worthy of note:
Conditional GAN (CGAN):
This is similar to the classic GAN but allows for the genera-
tion of data from a specific class defined within the real dataset, such as the generation
of MNIST digits conditioned on class labels [20].
Vanilla GAN:
This is a simple type of GAN where the discriminator and the generator are
simpler, multilayer perceptrons [1].

Appl. Sci. 2024,14, 7438 4 of 23
Deeper convolutional GAN (DCGAN):
This is a GAN whereby the architecture of both
the generator and discriminator is composed of ConvNets instead of multilayer
perceptrons [21–23].
Laplacian pyramid GAN (LAPGAN):
This combines the CGAN model with a Laplacian
pyramid representation [24].
Other known networks include: SRGAN, which employs a deep neural network [
25
];
StackGAN [
26
,
27
]; CycleGAN, as proposed in [
28
]; PassGAN [
29
]; WGAN, as seen in [
30
];
spatio–temporal GAN, as studied in [
31
]; constrained GAN, which can be seen in [
32
];
H-GAN [
33
]; pix2pix, as proposed in [
34
]; Android-GAN [
35
]; UNIT, which was developed
in [
36
]; RGAN and RaGAN, as introduced in [
37
]; and AnoGAN, as proposed in [
38
].
Ref. [
39
] introduces GANomaly and RCGAN, which are showcased in [
40
], and EGAN [
41
]
and TimeGAN are seen in [42], among others.
Another approach to addressing the limitations of GANs is through the proposal
of hybrid models that combine GANs with other deep learning models such as trans-
formers [
43
–
45
], diffusion models [
46
–
48
], and large language models [
49
,
50
]. This line of
research on the hybridisation of GANs with other architectures represents a promising field
of investigation.
Despite the continuous advancements and rapid growth that GANs have experienced
since their inception, several challenges remain to be addressed. These include the consis-
tency, the convergence rate, improving training stability and diversity, enhancing resources
and real-time scalability, and the need to establish robust ethical regulations.
2.3. Applications of GANs
GANs have been employed to address various types of problems:
•
Generating synthetic data for training: In [
13
,
51
], GANs are employed to generate
data related to lung cancer patients, and statistical tests are employed to validate such
synthetic data. For the generation of time-series data, one can employ SeqGAN [
52
].
It is also possible to generate synthetic data in tabular datasets using CTGAN [53].
•
Generating text and natural language: There are several notable examples: Seq-
GAN [
52
], which generates text sequences; LeakGAN [
54
], which introduces a search
policy to enhance text generation; and RankGAN [
55
]. Others, like textGAN [
56
],
are used for natural language generation. CTRL, proposed by [
57
], facilitates con-
trolled text generation and enables users to specify style and content. Additionally,
GPT-3, one of the most influential studies in text generation using GAN, is based on a
transformer architecture that produces high-level, coherent, and natural text [58].
•
Generating realistic images: One of the initial applications of GANs for generating
realistic images was introduced by [
21
], where the authors proposed a DCGAN. Sub-
sequently, other authors suggested different GAN modalities for distinct objectives.
For instance, [
28
] applied CycleGAN to generate images by learning domain corre-
spondence without the need for labelled data pairs. Another example is given by [
26
],
which used StackGAN to produce detailed, high-resolution images by introducing
a cascaded generator architecture. Other GAN variants, such as those employed for
generating human faces [
59
], include StyleGAN [
60
–
62
] and BigGAN [
63
]. These mod-
els are utilised to enhance the quality and resolution of generated images and employ
a scalable architecture. Other DCGAN variants are employed for the generation of
human poses from a photograph [
64
] and can even project the age progression of an
individual [65].
•
Enhancement and restoration of damaged or low-quality images: To address this issue,
several alternatives to the classic GAN have been proposed, such as SRGAN [
25
],
Pix2Pix [
34
], and CycleGAN [
28
]. The latter addresses inpainting tasks (by filling in
missing or damaged regions of an image) using DeepFill [
66
,
67
]. To restore images
that are corrupted by noise, RedNet was designed [
68
], and for noise filtering, nCNN
and FFDNet were created [69,70].

Appl. Sci. 2024,14, 7438 5 of 23
•
Speech synthesis: This constitutes one of the earliest application fields of GANs. For
instance, WaveGAN was designed for waveform-based voice synthesis [
71
]. Similarly,
MelGAN was developed for voice synthesis using high-quality Mel spectrograms,
and it provided enhanced quality and naturalness to synthesised voices [
72
]. To ex-
pedite real-time voice waveform generation, ParallelWaveGAN was proposed [
73
].
Other generative models include MelGAN-VC, which enables conversation [
74
],
and HiFi-GAN for high-fidelity voice synthesis, which provides greater quality and
detail [75,76].
GANs have also been used for the creation of music and songs by blend-
ing the different styles and compositions of recognised musicians and composers [
77
].
However, new approaches to apply GANs are constantly emerging, e.g., in anomaly
detection [
78
], data imbalance problems [
79
], probabilistic forecasting of financial time
series [45], stock price prediction [80], etc.
3. Statistics on GAN Publications
In this section, the evolution of scientific articles related to GANs is presented. To
this end, a search was conducted on 1 July 2023 using the Web of Science (WOS). In the
search field, the keyword used was “Generative Adversarial” or “generative adversarial”
or “GENERATIVE ADVERSARIAL”.
References to countries and research areas were extracted exactly as they appear in
the WOS.
The conducted search yielded a total of 18619 documents, categorised according to the
type of publication, as shown in Table 1.
Table 1. Frequency per type of document.
Document Type # Document Type # Document Type #
Articles 11,168 Meeting Abstracts 131 Letters 5
Proceedings 7027 Editorial Materials 21 Retracted Publications 3
Early Access 503 Corrections 17 Data Papers 2
Review Articles 335 Book Chapters 12 —
In this table, we can observe that 60% of GAN publications are in the form of papers,
and 38% are papers of proceedings.
Furthermore, in Table 2, the countries with the highest number of publications on this
topic are displayed (“Others” includes all countries with a percentage lower than 1%).
Table 2. Publications per country.
Country # % Country # %
China 9123 48.998 USA 3837 20.608
South Korea 1165 6.259 England 958 5.145
India 920 4.941 Japan 835 4.485
Germany 704 3.781 Australia 670 3.563
Canada 630 3.384 Taiwan 415 2.229
France 398 2.138 Singapore 369 1.982
Italy 350 1.880 Spain 296 1.570
Switzerland 251 1.348 Saudi Arabia 203 1.090
Others (103 countries) 3267 17.546 — — —
In this table, it can be observed that nearly half of all publications related to GANs
originate from China, which showcases the country’s positioning in emerging technologies.
With just over 20%, the USA ranks second and is significantly ahead of other countries.

Appl. Sci. 2024,14, 7438 6 of 23
3.1. Evolution of GANs
The evolution of GANs over time is depicted in Figure 2. In this figure, an estimation
of publications for the year 2023 is made based on a linear fit derived from the information
collected in the table for previous years. The years 2014 and 2015 were not considered
for the fit since the concept of GANs was still emerging and due to the lack of data to
accurately model the real growth (on 1 July 2023, there were 1825 publications in the
preceding 12 months).
Figure 2. Number of articles published per year.
It can be observed in this figure how the growth experienced by the papers addressing
GANs follows an increasing trend that has not yet reached its peak since there are still
fields, such as Business and Social Sciences, where the potential provided by this GAN tool
to the scientific world remains to be fully explored.
3.2. Knowledge Areas and GANs
Table 3has been obtained by taking into account the research areas collected by the
WOS and reveals the most relevant research areas (areas that comprise at least 1% of all
publications).
Regarding the areas that feature a significant number of scientific publications for these
GANs, the field of Computer Science stands out and encompasses a wide variety of applica-
tions in image processing, deep learning, and synthetic content
generation [21,28,34,63,81].
The field of Engineering presents multiple applications, ranging from the optimisa-
tion of renewable energies to manufacturing and monitoring of structural health, which
showcases its versatility and utility in solving real-world problems [
82
–
85
]. Of particular
interest is the application to cognitive cars [86–88] and electronic home appliances [89].
Another field where numerous applications are found is in Imaging Science and Pho-
tographic Technology. This field is undergoing continuous evolution, for which GANs are
contributing significant advancements [
25
,
90
–
95
]. It is a major challenge to enumerate the
multitude of applications and problems addressed in this field that have been successfully
resolved using GANs.
Work in Telecommunications also stands out [96–101].
In the field of Radiology, Nuclear Medicine and Medical Imaging, GANs appear partic-
ularly relevant regarding the processing and analysis of medical images by improving their
quality and usefulness, aiding in the diagnosis and treatment of diseases, and facilitating
advancements in research [102–107].

Appl. Sci. 2024,14, 7438 7 of 23
Table 3. Publications according to research areas.
Research Area # %
Computer Science 10,974 58.940
Engineering 8418 45.212
Imaging Science/Photographic Technology 2368 12.718
Telecommunications 1983 10.650
Radiology/Nuclear Medicine/Medical Imaging 1182 6.348
Remote Sensing 897 4.818
Optics 868 4.662
Physics 854 4.587
Chemistry 667 3.582
Instruments/Instrumentation 674 3.620
Automation/Control Systems 631 3.389
Materials Science 507 2.723
Geology 444 2.385
Mathematics 404 2.170
Science/Technology/Other Topics 404 2.170
Mathematical/Computational Biology 389 2.089
Environmental Sciences/Ecology 361 1.939
Acoustics 351 1.885
Neurosciences/Neurology 329 1.767
Geochemistry/Geophysics 336 1.805
Robotics 283 1.520
Medical Informatics 245 1.316
Transportation 235 1.262
Energy Fuels 212 1.139
Operations Research/Management Science 189 1.015
Others (90 areas) 2283 12.261
Of great significance is the field of Remote Sensing, where GANs have contributed
towards enhancing the quality and quantity of data, thereby enabling the interpretation
and analysis of images, and introducing new applications and techniques for monitoring
and understanding our planet from space [108–112].
GANs have overcome limitations in the field of Optics related to resolution and quality
in the reconstruction of diffracted images, thereby enhancing the capacity for observation
and analysis [113–115].
In the field of Physics, GANs have contributed towards significant advances in simu-
lation techniques, data analysis, and modelling across various domains, including compu-
tational physics, particle physics, and quantum physics [116–121].
In Chemistry, GANs have contributed towards advances in molecular techniques, drug
discovery, organic synthesis, and the prediction of chemical properties, thereby opening
doors for chemical and pharmaceutical research [122–125].
In the field of Instruments and Instrumentation, one can find studies applying GANs
in deepfake detection, synthetic data generation, instrument calibration, and improvements
in resolution in microscopy techniques [126–129].
In Automatic Control Systems, GANs have contributed innovative solutions in areas
such as anomaly detection, system modelling, and synthetic data generation for the design
and evaluation of control algorithms [130–135].
In Materials Science, GANs provide innovative solutions in areas such as material
discovery, molecular design, structure optimisation, and the generation of compounds with
specific properties [136–139].
In Geology, several significant applications are related to facies classification tech-
niques, geological pattern generation, image enhancement, and detection of geological
features [140–143].
In Mathematics, innovative solutions have been achieved in areas such as robust
optimisation, graph generation, geometric modelling, image compression, and solving

Appl. Sci. 2024,14, 7438 8 of 23
differential equations, expanding the capabilities and applications of GANs in the mathe-
matical domain [95,144–148].
In the field of Science, Technology and other Topics, advances have been made in
image generation techniques, enhancements in the quality and stability of GANs, as well
as in the manipulation and control of features in image synthesis. These advances have
propelled the field of computer vision and machine learning [28,59,60,63,149].
In Mathematical/Computational Biology, advances have been made in unsupervised
learning techniques, synthetic data generation, molecular modelling, and analysis of bi-
ological images, thereby opening new perspectives in mathematical and biological re-
search [21,150,151].
The application of GANs in Environmental Sciences and Ecology has developed inno-
vative solutions for species distribution modelling, land cover prediction, environmental
data analysis, and water quality monitoring, thereby enhancing our understanding and
management of ecosystems and the environment [152,153].
Also, in the field of Acoustics, GANs have contributed significant solutions in areas
such as audio synthesis, speech enhancement, sound simulation, and speaker verification,
thereby enhancing the quality and robustness of acoustic signal processing [71,154–158].
In the field of Neurosciences and Neurology, GANs have achieved advancements in
diagnostic techniques, synthetic data generation, analysis of brain signals, and the study of
neurological diseases, thereby providing new insights and tools for the field of neuroscience
and neurology [159–161].
When studying GANs in Geochemistry and Geophysics, significant advancements
have been achieved in techniques for the estimation of petrophysical properties, seismic
data inversion, interpretation of geophysical data, and modelling of subsurface structures.
These advances enhance our understanding and analytical capabilities in exploring and
characterising natural resources and geological processes [162–166].
In the field of Robotics, GANs have been applied in areas such as synthetic data
generation, reinforcement learning enhancement, robotic perception, terrain classification,
and robotic grasp improvement, thereby enhancing the capabilities and applications of
robots in various environments and tasks [167–171].
In the field of Medical Informatics, studies on GANs have developed innovative
solutions in areas such as the generation of synthetic medical images, anomaly detec-
tion, incomplete image reconstruction, and data synthesis to enhance the analysis and
interpretation of medical images, thereby significantly impacting disease diagnosis and
treatment [38,172–175].
In the field of Transportation, advances have been made in areas such as traffic flow
prediction, traffic simulation, traffic sign translation, vehicle re-identification, and estima-
tion of public transport demand, thereby enhancing the efficiency, safety, and sustainability
of transportation systems [176–180].
In the field of Energy Fuels, GANs have been applied in energy forecasting techniques,
optimisation of energy systems, design of renewable energy systems, and assessment of
climate-change mitigation strategies, thereby enhancing our ability to address current
energy and environmental challenges [181–184].
In Operations Research and Management Science, GAN applications encompass fields
such as synthetic data generation, revenue management, fraud detection, supply-chain
optimisation, and dynamic pricing. These applications improve decision-making and
efficiency in operations and management processes [185–188].
However, there are still significant fields where the development of such models can
offer major advances and advantages that are still emerging and hold great potential in their
research. One of these fields is Business Economics, where GANs can provide solutions to
major problems encountered in the economic world when conducting various studies of
interest. This topic is addressed in the following section.

Appl. Sci. 2024,14, 7438 9 of 23
4. Statistics on GAN Publications in Economics
In the field of Economics, GANs constitute an emerging and highly promising tool,
among other reasons due to their ability to generate synthetic data that can help to improve
economic research and analysis. Researchers often face limitations in data availability,
especially when dealing with economic data. It is worth noting that having access to
synthetic data in these scenarios would help simulate and predict complex economic
behaviours, thereby enabling the evaluation of various economic strategies and policies
well in advance to facilitate appropriate decision-making tailored to each situation.
In order to highlight these advances in the economic field and to reveal the extensive
potential this tool holds, we filter and analyse the selected bibliography by limiting it to
research directly related to this field. Hence, the number of obtained papers is reduced to a
total of 42 by the WOS in the the research areas considered: Business Economics (29), Social
Issues (3), Mathematics Methods in Social Science (7), and Social Sciences Other Topics (9).
Out of these 42 publications, we select the publications that are categorised as research
papers, narrowing down the number to 31. In order to analyse and present these research
contributions more clearly, these papers are grouped into five specific fields identified by
the researchers: Financial Economics (9), Management (4), Marketing and Publicity (8),
Logistics Transport (5), and Others (5). These areas are analysed in the following sections.
4.1. Financial Economics
The publications on GANs in the field of Financial Economics are displayed in
Table 4. The first paper [
189
] proposes an innovative optimisation framework for the
portfolio optimisation problem by leveraging GANs to develop a heuristic approach to the
classic Markowitz model. This involves three steps: (i) using a GAN to select the initial set
of assets for investment, (ii) solving the portfolio optimisation problem to determine the
weights of the assets from the previous step, and (iii) enhancing the obtained solutions.
In the year 2020, [
190
], proposed the use of CGAN for the calibration and aggregation
of trading strategies. They designed an experiment involving multiple trading strategies
across 579 assets and yielded successful results and clear advantages over other tools.
In [
191
], a data-driven approach is proposed to calibrate local stochastic volatility
models using GANs, and it demonstrates its feasibility and showcases several significant
advantages over other methods.
An approach using a GAN-based model, referred to as Quant GAN, is introduced
in [
192
] for modelling discrete-time financial time series. Although the models of time
series have historically been notably challenging to train, advances in GANs showcase
their potential to provide competitive outcomes. Thus, as training procedures continue to
evolve, GANs hold the promise of delivering even better performance in the future.
In [
193
], a GAN model named WCGAN-GP is developed to address the challenges
existing in actuarial domains, since actuaries often require a great amount of data. Further-
more, the datasets employed by actuaries frequently encounter issues with imbalanced
classes, wherein events of interest related to risk are underrepresented. The authors pro-
vide several contributions to actuaries, with applications including the generation of new
samples, data augmentation, enhancement of predictive models, anomaly detection, and
imputation of missing data.
Another paper from 2022 in the financial domain is [
194
], which addresses the problem
of maximising utility under uncertain parameters through a robust optimisation process
that can be interpreted as a stochastic differential game for two players with a zero-sum.
This algorithm is tested in real markets and shows that robust portfolios tend to have higher
expected utility and are more stable during market downturns. To solve the value function,
the authors derive the analytical solution in the case of logarithmic utility and obtain
numerical approximations using various methods, including GANs, and demonstrate a
high level of accuracy in the numerical results.

Appl. Sci. 2024,14, 7438 10 of 23
Table 4. WOS publications on Financial Economics.
Year Title Authors
2019 GAN-MP hybrid heuristic algorithm for non-convex
portfolio optimization problem. Kim, Y., Kang, D., Jeon, M., and Lee, C. [189]
2020 Generative adversarial networks for financial trading
strategies fine-tuning and combination. Koshiyama, A., Firoozye, N., and Treleaven, P. [190]
2020
A generative adversarial network approach to calibration of
local stochastic volatility models. Cuchiero, C., Khosrawi, W., and Teichmann, J. [191]
2020 Quant GANs: deep generation of financial time series. Wiese, M., Knobloch, R., Korn, R., and Kretschmer, P. [192]
2021 Alleviating class imbalance in actuarial applications using
generative adversarial networks. Ngwenduna, K. S., and Mbuvha, R. [193]
2022 Robust utility maximization under model uncertainty via a
penalization approach. Guo, I., Langrene, N., Loeper, G., and Ning, W. [194]
2022 DeepPricing: pricing convertible bonds based on financial
time-series generative adversarial networks. Tan, X., Zhang, Z., Zhao, X., and Wang, S. [195]
2022
Scenario generation for market risk models using generative
neural networks. Flaig, S., and Junike, G. [196]
2022
Simulating multi-asset classes prices using Wasserstein
generative adversarial network: A study of stocks, futures
and cryptocurrency.
Han, F., Ma, X., and Zhang, J. [197]
It is well-known that the major complexity in valuing convertible bonds lies in mod-
elling the underlying stock-return process. A GAN called DeepPricing that generates
financial time series capable of replicating a risk-neutral stock return process while preserv-
ing statistical properties is presented in [
195
]. The authors declare that it is more flexible
and accurate at capturing the dynamics of the underlying stock-return process.
The GANs used in [
196
] as generators of economic scenarios can be expanded into an
entire internal market risk model by encompassing a suitable number of risk factors. The
results obtained are comparable to regulatory-approved internal models in Europe. Hence,
GANs can be considered as an alternative method for market risk modelling. Furthermore,
the authors show how a GAN can serve as an economic scenario generator for market risk
calculation within insurance companies.
A Wasserstein generative adversarial network with gradient penalty (WGAN-GP)
is given in [
197
], where it is applied to the stock market, futures market, and cryptocur-
rency market. The results demonstrate that datasets generated from original asset prices
by WGAN-GP simulate asset prices well, thereby showcasing the potential of a market
simulator for trading analysis.
4.2. Management
All the studies in the field of Management found in the WOS are from the year 2022
and are presented in Table 5.
A study on the detection of credit card fraud using GANs to generate synthetic
samples by explicitly considering the distribution of customers is presented in [
186
]. The
conclusions state that using synthetically generated data has the potential to enhance model
performance, and that this performance is sensitive to underlying customer distributions
and data sources.
A framework is proposed in [
198
] to map the technological landscape of an emerging
industry value chain through deep-learning-based patent analysis by using a GAN as
a data augmentation method to overcome the issue of low-quality patent samples from
emerging industries. It is demonstrated that limitations regarding data from emerging
companies and class imbalance issues can be successfully addressed.

Appl. Sci. 2024,14, 7438 11 of 23
Table 5. WOS publications in Management.
Year Title Authors
2022
Generative adversarial networks for data augmentation and
transfer in credit card fraud detection. Langevin, A., Cody, T., Adams, S., and Beling, P. [186]
2022
Mapping the technological landscape of emerging industry
value chain through a patent lens: An integrated framework
with deep learning.
Xu, G., Dong, F., and Feng, J. [198]
2022 Responsible cognitive digital clones as decision-makers: a
design science research study.
Golovianko, M., Gryshko, S., Terziyan, V., and Tuunanen,
T. [199]
2022 An innovative machine learning model for supply chain
management. Lin, H., Lin, J., and Wang, F. [200]
In [
199
], a technology robot called Pi-Mind is developed and evaluated; it acts as a
responsible, resilient, ubiquitous cognitive clone (or a digital copy) and as an autonomous
representative of a human decision-maker. To train the Pi-Mind agent to choose the most
appropriate solution from among alternatives at critical decision points, the authors train
agents using GANs.
The last paper in this category is [
200
], the authors of which propose a method for
supply chain management. To tackle the challenge of a high number of decisions and
limited data samples, they propose a dynamic supply chain member selection algorithm
based on CGAN by dividing management into six areas: orders, purchases, production,
inventory, distribution, and transportation.
4.3. Marketing and Publicity
Table 6is obtained with respect to the field of Marketing and Publicity.
A comparative analysis is conducted in [
201
] on consumer preferences for a real
fashion product versus a synthetically generated product created using CycleGAN. It
was observed that the creation of new designs using GAN results in a higher perceived
value of the product, which potentially generates co-creation value for customers, thereby
enhancing their engagement and encouraging purchasing behaviour regardless of whether
they are aware of the use of technology. The study suggests that using GAN technology
can provide significant advantages by expanding the scope and scale of the product design
process and by increasing perceived consumer value.
In [
202
], deepfakes are introduced into the marketing literature by proposing a ty-
pology, a conceptual framework, and a research agenda based on balanced centrality.
This aims to guide future research on deepfakes in marketing studies, thereby allowing
both companies and customers to benefit from deepfakes while also safeguarding against
potential risks.
The study presented by [
203
] proposes a mechanism to automate the generation
of textual ads and combines a GAN model with reinforcement learning to optimise the
generation of advertising texts.
Along the same lines as the aforementioned articles, Ref. [
204
] constructs a general
framework to better understand consumers’ responses to all forms of advertising manipu-
lation; the researchers are aided by digital and automatic tools that enable advertisers to
automate many advertising processes and to produce increasingly sophisticated synthetic
ads. These same authors continue by analysing the impact of this increasingly pervasive
trend of ad automation. In Ref. [
205
], the authors show that these AI-based tools, in the
same way as GANs, may bring potentially drastic changes to the way ads are conceived,
produced, edited, and directed. They also examine the associated ethical issues.

Appl. Sci. 2024,14, 7438 12 of 23
Table 6. WOS publications on Marketing and Publicity.
Year Title Authors
2021
Artificial intelligence in the fashion industry: consumer
responses to generative adversarial network (GAN)
technology.
Sohn, K., Sung, C. E., Koo, G., and Kwon, O. [201]
2021 The rise of deepfakes: A conceptual framework and
research agenda for marketing. Whittaker, L., Letheren, K., and Mulcahy, R. [202]
2022 Ad creative generation using reinforced generative
adversarial network. Terzioglu, S., Cogalmis, K. N., and Bulut, A. [203]
2022
Preparing for an era of deepfakes and AI-generated ads: A
framework for understanding responses to manipulated
advertising.
Campbell, C., Plangger, K., Sands, S., and Kietzmann,
J. [204]
2022
How deepfakes and artificial intelligence could reshape the
advertising industry: The coming reality of AI fakes and
their potential impact on consumer behavior.
Campbell, C., Plangger, K., Sands, S., Kietzmann, J., and
Bates, K. [205]
2022 Using deep learning to overcome privacy and scalability
issues in customer data transfer. Anand, P., and Lee, C. [206]
2023
Product aesthetic design: A machine learning augmentation.
Burnap, A., Hauser, J. R., and Timoshenko, A. [207]
2023
Towards privacy-preserving digital marketing: an
integrated framework for user modeling using deep
learning on a data monetization platform.
Han, Q., Lucas, C., Aguiar, E., Macedo, P., and Wu, Z. [
208
]
Another important aspect in this field is customer privacy. Ref. [
206
] demonstrate
that recent advances in machine learning enable companies to transfer a generative model
instead of using real data. They show the effectiveness of GANs for preserving the desired
characteristics of the original data, which offers advantages both in terms of privacy and
scalability. GANs outperform benchmark models in solving marketing problems and
alleviate the logistical and computational burden for data providers since they only need
to train one GAN model that can solve various marketing problems. This approach is
advantageous in terms of both volume and speed.
To influence the aesthetic design process, Ref. [
207
] propose a model that combines
a probabilistic variational autoencoder (VAE) with adversarial components from a GAN
and a supervised learning component. The model is tested in the automotive sector
and demonstrates a significant improvement compared to conventional machine learning
models and neural networks.
Lastly in this section, the work by [
208
] introduces an innovative approach to user
modelling that preserves privacy for digital marketing campaigns by combining learning
techniques and GANs. This allows users to retain control of their personal data while
enabling marketing professionals to identify suitable behaviours for their campaigns.
4.4. Logistic Transport
In the field of Logistic Transport, there are several papers available (see Table 7).
These studies address the challenge of anticipating potential accidents. However, they
encounter imbalanced and limited datasets, leading to a low incident-detection rate and
a high false-alarm rate in detection models. In this context, the study by [
209
] proposes a
new incident detection framework based on GANs. Experimental results demonstrate a
significant enhancement in the detection rate and a reduction in false-alarm rates.
In the paper by [
210
], the issue of requiring a large labelled dataset regarding travel
modes to avoid congestion is addressed. Often, these travel modes are unbalanced in their
representation. Hence, the authors develop a hybrid travel-mode detection model using
neural networks and GANs. The GANs are employed to augment the size of the dataset
and balance it, thereby enhancing the accuracy of the detection model.

Appl. Sci. 2024,14, 7438 13 of 23
Table 7. WOS publications on Logistic Transport.
Year Title Authors
2020 Automated traffic incident detection with a smaller dataset
based on generative adversarial networks. Lin, Y., Li, L., Jing, H., Ran, B., and Sun, D. [209]
2020
Coupled application of generative adversarial networks and
conventional neural networks for travel mode detection
using GPS data.
Li, L., Zhu, J., Zhang, H., Tan, H., Du, B., and Ran, B. [210]
2021 A deep learning approach for real-time crash prediction
using vehicle-by-vehicle data. Basso, F., Pezoa, R., Varas, M., and Villalobos, M. [211]
2022 Transfer learning for spatio-temporal transferability of
real-time crash prediction models. Man, C. K., Quddus, M., and Theofilatos, A. [212]
2022 Generating mobility networks with generative adversarial
networks.
Mauro, G., Luca, M., Longa, A., Lepri, B., and Pappalardo,
L. [213]
On the other hand, in order to address the concern of predicting collisions in real-time,
Ref. [
211
] proposes a new data architecture inspired by images that is capable of capturing
the microscopic scene in which vehicular interactions occur. For this purpose, an accident
prediction model is constructed using multi-input convolutional neural networks and by
employing various oversampling methodologies to balance the training data. The authors
ascertain that the best results are achieved using deep convolutional generative adversarial
networks (DCGANs) with random undersampling.
The same topic is addressed in [
212
], but from the perspective of spatio–temporal
transferability. A combination of a GAN and transfer learning is used to examine the
transferability of real-time collision prediction models in an extremely imbalanced data
environment. The practical application involves using a Wasserstein GAN (WGAN) to
generate synthetic collision data since the initial dataset had an extreme imbalance between
collision and non-collision data. The study revealed that direct transferability is not feasible.
However, the models become spatio–temporally transferable in terms of time and space
when transfer learning is applied.
The last study obtained in this field, by [
213
], proposes a MoGAN model to generate
realistic urban mobility networks. This is applied to public bicycle and taxi travel data
and demonstrates increased realism compared to classical gravity and radiation models.
Moreover, it can be used for data augmentation, simulations, and hypothetical analyses.
4.5. Others
Finally, those studies that did not directly correspond to Economics were grouped
under the title “Others” (see Table 8).
A GAN model called Lung-GAN is introduced in [
214
] and is trained to interpret
images of lung disease to enable early diagnosis, thereby improving survival rates.
In [
215
], the missing-data problem is addressed due to the lack of responses in survey
sampling using deep-learning models.
In [
216
], the use of GANs for cybersecurity analysis is proposed and addresses the
challenge of the limited labelled data used by current machine learning models trained
by humans.
The aim of [
217
] involved fall prevention to reduce medical costs. These authors utilise
a hidden Markov model with a generative adversarial network (HMM-GAN).
In [
218
], the origins of deepfakes are traced back to the inception of GANs; this
phenomenon is reviewed, and the authors present recent statistics, prevalent applica-
tion domains, risks, and opportunities. The authors analyse the latest bibliography and
highlight the novelty of a scenario where falsehoods in human societies and cultures are
predominantly produced by machines. This paper underscores the importance of a semiotic
and interdisciplinary study of these productions.

Appl. Sci. 2024,14, 7438 14 of 23
Table 8. WOS publications classified as “Others”.
Year Title Authors
2021 Lung-GAN: Unsupervised representation learning for lung
disease classification using chest CT and X-ray images. Yadav, P., Menon, N., Ravi, V., and Vishvanathan, S. [214]
2022
Are deep learning models superior for missing data
imputation in large surveys? Evidence from an empirical
comparison.
Wang, Z., Akande, O., Poulos, J., and Li, F. [215]
2022 Cross-lingual cybersecurity analytics in the international
dark web with adversarial deep representation learning. Ebrahimi, M., Chai, Y., Samtani, S., and Chen, H. [216]
2023
Motion Sensor-based fall prevention for senior care: A
hidden Markov model with generative adversarial network
approach.
Yu, S., Chai, Y., Samtani, S., Liu, H., and Chen, H. [217]
2023 The spiral of digital falsehood in deepfakes. Leone, M. [218]
5. Why Use GANs in Business Economics?
Given that the main aim of GANs involves the automatic generation of data, we ask
when and how can they be used in Economics. These networks can be utilised to expand
the size of the working set, since data availability in most economic problems is often scarce
yet crucial. This helps researchers to develop mechanisms that are essential for making
economic, business, and commercial decisions, which is pivotal for the proper functioning
of the sector.
On the other hand, anomaly detection is highly sought-after in this field for the
detection of credit card fraud, insurance fraud, and banking operations. In these problems,
there is often an imbalance issue, with only a small amount of data in the relevant class
compared to abundant data in the other class. In this scenario, GANs can be conditioned
to generate data for the minority class since it is well-known that models perform better
when data are balanced.
Another issue that can be addressed using GANs is that of time-series data, which can
simulate temporal series and stochastic processes for risk management, financial projections,
stock prediction, monetary policy formulation, price evolution, capital modelling, solvency
projection, reserve management, asset and liability management, and mortality projections,
among others.
Another significant issue that can be addressed by GANs is privacy preservation, given
that most business data and other information may be secret, confidential, and/or sensitive.
For instance, a company such as a private hospital could withhold its real patient database
by generating a set of synthetic data with the same characteristics as the real patients, even
though these patients do not exist. This method preserves privacy while allowing the use
of data to create models that improve the practices of this and other hospitals.
Another problem is that of missing data, which affects the field of business economics
and poses a challenge when applying analytical methods that are often unprepared for
missing datasets.
One of the key advantages GANs offer over conventional methods in solving these
aforementioned issues involves their operation through supervised learning, which en-
hances the performance of the models.
6. Conclusions
Throughout the numerous publications analysed, the success of the application
of GANs for addressing various problems in many diverse areas of knowledge has
been demonstrated.
However, it has been shown that GANs have hitherto been underutilised in issues
related to the Economic Business field.
Given the range of economic problems with which GANs can assist researchers and can
yield promising results, often surpassing those achieved by current techniques, we believe
that GANs should be more frequently employed. GANs could offer a new perspective on

Appl. Sci. 2024,14, 7438 15 of 23
several of the most current and diverse issues in this area and could potentially provide
a range of superior outcomes in many situations compared to the outcomes of existing
methodologies. It has also been observed that some papers related to Economics are
categorised under other areas of knowledge. For instance, Refs. [
219
–
225
] are all listed
under the area of Computer Science. However, even with a change in these categorisations,
the utilisation of GANs in Economic problems still remains very limited.
Author Contributions: Conceptualization, L.G.-A. and A.R.-G.; methodology, L.G.-A. and A.R.-G.;
software, L.G.-A. and A.R.-G.; validation, L.G.-A. and A.R.-G.; investigation, L.G.-A. and A.R.-G.;
resources, A.R.-G.; writing—original draft preparation, A.R.-G.; writing—review and editing, A.R.-G.
and L.G.-A.; visualization, L.G.-A.; supervision, A.R.-G.; project administration, L.G.-A.; funding
acquisition, L.G.-A. and A.R.-G. All authors have read and agreed to the published version of
the manuscript.
Funding: This research was partly funded by the Spanish Ministry of Science, Innovation and Uni-
versities (AEI/FEDER, UE) under grant number PID2022-141045OB-C42 (research project: generation
of reliable synthetic health data for federated learning in secure data spaces).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is contained within the article.
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
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