ARTIFICIAL INTELLIGENCE AND CYBERSECURITY IN BANKING SECTOR: OPPORTUNITIES AND RISKS

Eighth International Scientific Conference Contemporary Issues in Economics, Business and Management [EBM 2024], [pp. 425-433]

AUTHOR(S) / AUTOR(I): Ana Kovačević , Sonja D. Radenković , Dragana Nikolić

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DOI: 10.46793/EBM24.425K

ABSTRACT / SAŽETAK:

The rapid advancements in artificial intelligence (AI) have presented new opportunities for enhancing efficiency and economic competitiveness across various industries, espcially in banking. Machine learning (ML), as a subset of artificial intelligence, enables systems to adapt and learn from vast datasets, revolutionizing decision-making processes, fraud detection, and customer service automation. However, these innovations also introduce new challenges, particularly in the realm of cybersecurity. Adversarial attacks, such as data poisoning and evasion attacks, represent critical threats to machine learning models, exploiting vulnerabilities to manipulate outcomes or compromise sensitive information. Furthermore, this study highlights the dual-use nature of AI tools, which can be used by malicious user. To address these challenges, the paper emphasizes the importance of developing machine learning models with key characteristics such as security, trust, resilience and robustness. These features are essential to mitigating risks and ensuring secure deployment of AI technologies in banking sectors, where the protection of financial data is paramount. The findings underscore the urgent need for enhanced cybersecurity frameworks and continuous improvements in defensive mechanisms. By exploring both opportunities and risks, this paper aims to guide the responsible integration of AI in the banking sector, paving the way for innovation while safeguarding against emerging threats.

KEYWORDS / KLJUČNE REČI:

Artificial Intelligence, Machine Learning, Cyber Security, Adversarial Attacks, Banking

ACKNOWLEDGEMENT / PROJEKAT:

This work was supported by the Science Fund of the Republic of Serbia under Grant 7749151 within the Framework of the IDEAS Program—Management of New Security Risks Research and Simulation Development, NEWSiMR&D.

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