Applicability Of Industry 4.0 Technologies in Combating Fraud in the Financial System

Abstract

The increasing digitalization of financial services has brought significant benefits, such as speed and convenience,
but it has also increased exposure to sophisticated fraud schemes, including identity theft, card fraud, and money laundering.
In this context, Industry 4.0 (I4.0) technologies—particularly Machine Learning (ML) and blockchain—have emerged as
strategic tools for enhancing security and mitigating risks in the banking sector. This study aims to examine the applicability
of these technologies in detecting and mitigating financial fraud, addressing the following research question: How have
blockchain and ML technologies contributed to mitigating different categories of fraud? To answer this question, a
Systematic Literature Review (SLR) was conducted using the Scopus and Web of Science databases, focusing on open-access
articles published between 2021 and 2025 that were aligned with the research topic. Following the PRISMA protocol, 27
articles were selected and analyzed through bibliometric analysis, content analysis, and mapping of future research
directions. The originality of this research lies in the integration of cutting-edge ML and blockchain analyses to mitigate
financial fraud, bridging technology, challenges, and gaps. Despite these advances, challenges remain, including data
imbalance, the need for model interpretability, and ethical and regulatory concerns. Future research should focus on
integrating ML and blockchain, improving algorithms to handle imbalanced datasets, and developing explainable and secure
solutions. This study contributes by providing an up-to-date and comprehensive overview of current trends and research
gaps in the application of I4.0 technologies to financial security, serving as a foundation for scientific progress and the
adoption of these tools by banking institutions.