What question did this study set out to answer?

To develop an AI-based cybersecurity framework that addresses technical and organizational challenges in banking.

December 11, 2025

An AI ‐Based Framework for Secure and Transparent Banking: Integrating Adversarial Robustness, Interpretability, and Organizational Modeling

Puntos clave

To develop an AI-based cybersecurity framework that addresses technical and organizational challenges in banking.
Integrated adversarial robustness, explainable AI, and agent-based modeling to enhance cybersecurity.
Utilized interpretability tools like SHAP and LIME for better decision-making under threats.
Validated framework with adversarial simulations and performance metrics for accuracy and feature stability.
Achieved 94.1% accuracy and 92.8% F1 Score in detecting cyber threats.
Demonstrated improved transparency and resilience in responses to cyber attacks.
Maintained 89% feature stability during FGSM and PGD attacks.

Resumen

ABSTRACT This paper presents an integrated AI‐based cybersecurity framework for banking institutions that addresses technical and organizational challenges. Unlike traditional security systems that rely solely on detection accuracy, the proposed framework unifies adversarial robustness, explainable AI (XAI), and agent‐based modeling (ABM) to detect and simulate responses to cyber threats. It emphasizes the role of knowledge management by embedding interpretability tools, such as SHAP and LIME, and modeling human decision‐making under threat conditions. We validated the framework using adversarial simulations with FGSM and PGD, SHAP‐based interpretability testing, and ABM‐driven response modeling. The approach demonstrates favorable results in improving transparency, resilience, and adaptive response capacities. Our model achieved 94.1% accuracy and 92.8% F1 Score while maintaining 89% SHAP feature stability against FGSM and PGD attacks.

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