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FINANCIAL GANS AS DIGITAL INFRASTRUCTURE:
PRIVACY-PRESERVING SYNTHETIC DATA FOR ECONOMIC
TRANSFORMATION

1
Boudaoud Arbia* , Salheddine Kabou 2

1 University of Ahmed DRAIA Adrar, Adrar, Algeria.
2 Higher Normal School of Bechar, Bechar, Algeria.
(*E-mail: bouda.arbia@univ-adrar.edu.dz)

ABSTRACT
Access to representative, high-quality financial data is essential for risk
management, regulatory supervision, and fintech innovation, yet confidentiality
constraints severely limit its reuse. This paper surveys the state of the art in Financial
Generative Adversarial Networks (Financial GANs) and positions synthetic-data
generation as a practical, privacy-preserving component of institutional data
infrastructure. We present a taxonomy of generative architectures including
autoregressive/recurrent generators, conditional/hybrid time-series models,
transformer/attention-based frameworks, and privacy-focused variants and summarize
which stylized facts (autocorrelation, volatility clustering, heavy tails, cross-asset
dependence, tail risk) each family best reproduces. Building on an evaluation framework
tailored for infrastructure readiness, we recommend a task-oriented benchmarking recipe
that blends statistical diagnostics (moments, KS/MMD tests), temporal/pathwise checks
(autocorrelation, signatures), downstream utility (train-on-synthetic / test-on-real), and
risk metrics (VaR/CVaR, tail overlap). Finally, we enumerate deployment and governance
requirements for production use and discuss practical tradeoffs between fidelity, privacy,
and operational cost. Together, these elements create a roadmap for safely integrating
Financial GANs into supervisory sandboxes, model validation workflows, and cross-
institutional research while preserving measurable privacy guarantees.
Keywords: Data infrastructure; differential privacy; financial time series; generative
adversarial networks; synthetic data generation; time-series modeling.

1. Introduction
Access to high-quality, privacy-preserving financial data is a foundational
requirement for robust risk management, effective supervision, and rapid fintech
innovation. Transactional records and market time series power credit scoring, stress
testing, fraud detection, and model validation, yet strict confidentiality and regulatory
constraints often prevent their broad reuse. Synthetic data can reconcile the twin
objectives of utility and privacy by enabling experimentation, model development, and
cross-institutional analysis without exposing raw customer information.
Generative Adversarial Networks (GANs) have emerged as a leading approach for
synthetic-data generation. A GAN trains two neural networks in opposition: a generator
that produces candidate samples, and a discriminator that attempts to distinguish
generated from real data. While GANs were first developed for image synthesis,
adaptations for sequential and tabular financial data incorporate temporal
encoders/decoders, conditional inputs (market regimes, covariates), and privacy-
preserving training recipes. These adaptations make GANs capable of modeling complex

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