AI-First Banking: Ethical Model and Real-Time Cyber Decision Infrastructure Empowered by AI-Powered LLM-Generated Legal Briefs
DOI:
https://doi.org/10.15662/IJRAI.2022.0506016Keywords:
AI governance, banking ethics, real-time cyber decisioning, SIEM, LLM legal briefs, human-in-the-loop, explainability, fairness, compliance automationAbstract
Financial institutions are rapidly adopting large language models (LLMs) and other AI systems to automate decision-making, speed legal review, and harden cyber-defense. This paper proposes an integrated AI-First Banking framework that combines (1) an ethical governance model tailored for banking contexts, (2) a real-time cyber decision infrastructure that fuses streaming telemetry with AI reasoning, and (3) an operational pipeline for LLM-generated legal briefs to accelerate compliance and incident response. The ethical model adapts established AI principles (fairness, explainability, privacy, accountability) to banking-specific risks such as credit discrimination, market manipulation, and privacy leakage. The cyber decision infrastructure is an event-driven, layered architecture that ingests network/systems telemetry, applies hybrid analytic engines (rule-based + ML anomaly detectors), and exposes a decision fabric that issues prioritized, explainable remediation actions. LLMs assist legal and compliance teams by producing structured legal briefs, summarizing regulations, and drafting incident notifications; these outputs are constrained by a verification loop that includes retrieval-augmented evidence checks and human-in-the-loop legal validation to prevent hallucination and legal risk. We present a research methodology combining simulation, red-team cyber exercises, and mixed-methods evaluation (quantitative metrics for detection/response time, false positive/negative rates, and qualitative assessment of legal brief accuracy and practitioner trust). Results from prototype simulations show improved time-to-containment and higher triage accuracy vs. baseline SIEM workflows, while LLM-assisted legal drafting reduced drafting time substantially but required mandatory lawyer sign-off. We close by discussing tradeoffs, regulatory implications, and a roadmap for future deployment that emphasizes auditability, continuous monitoring, and cross-functional governance. The paper contributes a practical, ethically framed blueprint for integrating cutting-edge AI into banking operations while retaining human oversight and legal safety nets.
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