Adversarial-Resilient and Explainable AI Frameworks for Deepfake-Aware Fraud Detection in Financial Systems: A Comprehensive Review

In conclusion, this review highlights the growing necessity for adaptive, secure, and trustworthy AI frameworks as artificial intelligence transitions from reactive models to highly autonomous and agentic systems operating in dynamic and high-stakes environments. The analysis underscores that the increasing complexity and autonomy of AI—enhanced through generative models, Model Context Protocol (MCP), and applied machine learning—demand corresponding advancements in governance, explainability, and zero-trust security principles to ensure responsible and reliable behavior. Comparative evaluation of state-of-the-art cybersecurity and cryptographic techniques further demonstrates the effectiveness of deep learning, anomaly-detection algorithms, hybrid encryption, and blockchain-based authentication, with accuracies ranging from 94% to over 99%, illustrating substantial progress in securing modern AI ecosystems. Overall, the findings affirm that integrating adaptive protocols, cross-layer security, and context-aware intelligence offers a robust pathway for developing AI systems that remain resilient, ethically aligned, and trustworthy throughout their lifecycle, even as technological and environmental challenges continue to evolve.

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