Ethical AI-First Banking: Cloud-Powered Cyber Decision Infrastructure with Quantum Machine Learning and Agentic Negotiation Frameworks
DOI:
https://doi.org/10.15662/IJEETR.2022.0404003Keywords:
Ethical AI, cloud-native banking, cyber decision infrastructure, quantum machine learning, agentic negotiation, fairness, explainability, zero-trust, privacy-preserving analyticsAbstract
This paper proposes an integrated architectural and methodological blueprint for ethical, AI-first banking that combines cloud-native cyber decision infrastructure with quantum-enhanced machine learning and agentic negotiation frameworks. We argue that the next generation of financial institutions must move beyond isolated AI or cloud pilots to a cohesive stack where data governance, privacy-preserving analytics, automated negotiation agents, and quantum-accelerated learning jointly enable resilient, auditable, and value-aligned banking operations. The proposed architecture layers a secure, zero-trust cloud foundation under a policy-aware data plane that enforces consent, provenance, and explainability. On top of this plane we integrate quantum machine learning (QML) modules for high-dimensional pattern discovery in risk, fraud, and liquidity forecasting, while hybrid classical-quantum pipelines maintain practical performance on near-term hardware. Agentic negotiation frameworks govern automated inter-agent transactions — for example, credit-pricing negotiation between customer agents and bank policy agents — using constrained multi-agent reinforcement learning with explicit ethical and regulatory reward shaping. We present a research methodology to evaluate the architecture across simulation, synthetic-augmented production traces, and a controlled pilot with human-in-the-loop oversight, measuring metrics for fairness, robustness, auditability, latency, and cost. Results from simulation show improved detection of coordinated fraud patterns at lower false-positive rates, and agentic negotiation increased market-optimal outcomes while respecting exogenous fairness constraints. We discuss ethical trade-offs, regulatory alignment, implementation challenges, and a roadmap to transition from pilot to production. The contribution is a unified design and evaluation strategy that helps banks adopt AI in ways that are both performant and demonstrably aligned with ethical and supervisory expectations.
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