AI-First Banking: Ethical Model and Cyber Decision Infrastructure for Integrating Legacy ERP in the Serverless Revolution with AI-Guided Intelligence
DOI:
https://doi.org/10.15662/IJARCST.2022.0505004Keywords:
AI governance, serverless architecture, legacy ERP integration, cyber decision infrastructure, ethics, banking, Oracle EBS, data mesh, explainability, zero-trustAbstract
The accelerating shift to serverless architectures and AI-guided automation is reshaping financial services, but legacy ERP systems and entrenched on-premise workflows remain central to most banks’ operations. This paper proposes an AI-First Banking conceptual model that tightly couples an ethics-aware AI governance layer with a cyber decision infrastructure (CDI) to safely integrate legacy ERP (including Oracle EBS and similar suites) into serverless, event-driven ecosystems. The model addresses three core challenges: (1) bridging data, process and identity heterogeneity between legacy ERP modules and cloud-native functions; (2) enforcing ethical, privacy and compliance constraints while enabling adaptive AI decisions; and (3) providing resilient, explainable cyber-decision support for runtime threat detection and response. We describe an architecture composed of (a) a lightweight ERP adaptor and canonical data mesh for semantic normalization, (b) an AI policy engine that enforces context-aware ethical constraints (privacy, fairness, regulatory rules) and produces auditable decision artifacts, and (c) a distributed CDI that combines event stream analytics, real-time threat scoring, and playbook orchestration for automated containment. The approach uses serverless compute and managed services to reduce operational overhead, while employing layered security (zero-trust, encryption in motion and at rest, hardware attestation) and provenance tracking for auditability. We illustrate the model with two applied scenarios — automated credit decision augmentation and PAYG fraud response integration with legacy settlement modules — and report qualitative evaluations that show improved throughput, reduced mean time to containment in simulated incidents, and stronger regulatory traceability compared with conventional lift-and-shift integrations. We close with an implementation roadmap, limitations, and recommended avenues for future validation, including controlled field trials and human-in-the-loop governance experiments. The contribution is a practical blueprint for banks seeking to combine the agility of serverless AI with the realities of ERP-centric core systems without compromising ethics or cyber resilience.
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