Towards Transparent AI-Powered Cybersecurity in Financial Systems: The Deployment of Federated Learning and Explainable AI in the CaixaBank pilot - Karampasi, Aikaterini; Radoglou-Grammatikis, Panagiotis; Pawlicki, Marek; Choras, Ryszard; de Pozuelo, Ramon Martin; Sarigiannidis, Panagiotis; Puchalski, Damian; Pawlicka, Aleksandra; Kozik, Rafal; Choras, Michal (IEEE ... International Conference on Data Mining workshops, 2024)
In the domain of financial cybersecurity, where trust and reliability is paramount, the advent of Artificial Intelligence is bringing novel tools for network intrusion detection. This paper introduces AI4FIDS, a novel AI-powered Intrusion Detection System leveraging Federated Learning (FL) to enhance data privacy while enabling decentralized model training across multiple financial entities. Concurrently, we present TRUST4AI.xAI, an explainability module designed to render AI decision-making transparent and interpretable, thereby aligning with the critical need for model accountability in financial applications. Our experimental results, conducted in the framework of the AI4CYBER project's financial sector pilot, demonstrate in detecting network intrusions in financial infrastructure while maintaining user privacy, while increasing trustworthiness via explain-ability methods. The integration of these technologies addresses the dual challenges of effective threat detection and regulatory compliance, offering a scalable solution for modern financial institutions. This work contributes to the ongoing dialogue on leveraging AI for financial security and sets a benchmark for the development of privacy-preserving, interpretable AI models in this sector.