Enhancing Artificial Financial Intelligence: a Cybersecurity Framework Powered by Intelligence Insight

The transformative potential of Artificial Intelligence (AI) in Financial Technology (FinTech) is undeniable and unprecedented, namely in terms of operational efficiency, fraud prevention and customer-centric innovations just to name a few. At the same time, the rapid integration of AI into financial systems introduces unique risks, peculiar to the cyber sphere in which the technology operates, including but not limited to augmented exposure to adversarial attacks, data poisoning, and model exploitation. These vulnerabilities not only threaten operational integrity but also hinder the full realization of AI’s capabilities in secure, trustworthy environments. This paper aims to address these challenges by proposing the adoption of intelligence-driven cybersecurity frameworks, a strategic approach designed to enhance the resilience of AI systems and unlock their optimal efficiency and potential for financial institutions. Thus, the research critically examines the intersection of AI, FinTech, and cybersecurity, firstly from an exploration of the risks introduced by AI systems in financial applications. Although current approaches to risk mitigation provide adequate solutions, it is beneficial to shed light on critical gaps such strategies might still carry, particularly in their ability to respond to rapidly evolving and adaptive threats. To bridge these gaps, this study supports budding yet driven research approaches that revolve around intelligence-focused cybersecurity frameworks as a robust and adaptive solution. Drawing from cyber threat intelligence (CTI) principles and strategic intelligence methodologies, the framework emphasizes proactive threat identification, enhanced system monitoring, and informed decision-making. By integrating enriched threat intelligence into cybersecurity strategies, a framework of the sort enables FinTech entities to anticipate and mitigate evolving threats, ensuring the resilience and adaptability of AI systems. Most notably, such strategy would not only address security challenges but also foster trust and regulatory compliance, thereby enabling AI systems to operate at their full potential. These theoretical insights are grounded into practice through the analysis of relevant case studies, examining successful applications of intelligence-driven tools in secure FinTech environments. These examples underscore the dual benefit of risk mitigation and enhanced operational efficiency, demonstrating how robust cybersecurity serves as an enabler rather than an obstacle to AI innovation. Finally, these findings are contextualized within the broader regulatory landscape, aligning the proposed framework with international standards and regulations. Intelligence-driven strategies are shown to complement regulatory compliance, therefore fostering a secure, innovative, and sustainable AI-driven FinTech ecosystem. The study is structured with an initial introduction on the practical challenge of leveraging AI’s transformative potential while addressing its inherent cybersecurity risks. Subsequently, such vulnerabilities specific to AI applications in FinTech are explored. This overview serves as the bridge to present the intelligence-driven framework, highlighting its strategic dimensions for risk management and operational resilience. Empirical evidence through relevant case studies is then incorporated in order to support the framework’s practical value. Further, these insights are situated within regulatory contexts, emphasizing the alignment of security and compliance goals. Finally, the conclusion revolves around a forward-looking discussion on the future role, as well as potential complexities and pitfalls, of intelligence-driven approaches in securing AI’s innovative perspectives for financial systems.