HomeJournalsAMLIDVol. 1, Iss. 1AI-Driven Financial Security: Innovations in Prote
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Research ArticleAdvances in Machine Learning, IoT and Data Security

Volume 1, Issue 1 · 28 March 2026

ISSN: 3067-5529 · E-ISSN: 3067-5545

AI-Driven Financial Security: Innovations in Protecting Assets and Mitigating Risks

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Mani Prabha:1Department of Business Administration, International American University, Los Angeles, CA 90010, USA
MD. Jahid Hassan:Department of Information and Communication Technology, Islamic University, Kushtia -7003 Bangladesh
Jarin Tias Meraj:Department of Computer Science and Engineering, Daffodil International University, Birulia, Savar, Dhaka-1216, Bangladesh
Article ID:amlids24004

Abstract

The financial sector encounters numerous challenges such as cyber threats, fraud, and regulatory compliance. Traditional methods of safeguarding financial transactions and assets are becoming increasingly insufficient against advanced cyber-attacks. This thesis examines the transformative impact of Artificial Intelligence (AI) on financial security. It investigates various AI-driven innovations, their applications in asset protection, and risk mitigation, while also considering the ethical and regulatory implications. AI is reshaping financial risk management by offering advanced tools and techniques for identifying, assessing, and mitigating risks. This article explores the innovations and applications of AI-driven financial risk management, emphasizing its transformative effect on traditional risk management practices. We discuss various Artificial intelligence technology, such as natural language processing, predictive analytics, and machine learning and their applications in enhancing financial stability, regulatory compliance, and operational efficiency. As cyber threats grow more sophisticated, traditional network security approaches are becoming inadequate due to scalability issues, slow response times, and the inability to detect advanced threats. This highlights the need for research into more efficient security methods to protect against diverse network attacks. Cybercriminals use AI for data poisoning and model theft to automate attacks, emphasizing the need for AI-based cybersecurity techniques. This study introduces a cybersecurity technique based on AI for financial sector management (CS-FSM) to map and prevent unforeseen risks. By utilizing AI technologies like the K-Nearest Neighbor (KNN) algorithm with the Enhanced Encryption Standard (EES), the suggested approach improves data privacy, scalability, risk reduction, data protection, and attack avoidance, significantly improving the performance of cybersecurity systems in the financial sector.

Keywords

Artificial Intelligence (AI)Financial securityAsset protectionRisk reductionNetwork security
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Article Information

Received

9 July 2024

Accepted

13 August 2024

Published

28 March 2026

ISSN

3067-5529

E-ISSN

3067-5545

Article Type

Research Article

Open Access

Yes – Open Access