The Architecture of AI Governance in Banking: Bridging Compliance and Explainability Gaps (Pacific Journal of Advanced Engineering Innovations)

Summary

• AI governance integrates explainability, model risk management, lifecycle governance, and internal controls to satisfy regulatory expectations in banking.p.1
• Regulatory guidance such as SR 11 7 highlights the necessity of lifecycle-based model risk management covering development, validation, deployment, and continuous monitoring.p.2
• Governance explainability requires banks to document approval rationales, testing methodology, assumptions, and performance metrics throughout the model lifecycle.p.2
• Implementation patterns in banking prioritize high-stakes use cases like credit decisioning due to direct consumer and regulatory impacts.p.4
• Technical integration often relies on standard machine learning frameworks such as scikit learn, XGBoost, and TensorFlow combined with microservice architectures.p.5
• Evaluating third-party AI requires evaluating vendor model performance, limitations, data integrity, update mechanisms, and contractual accountability.p.6

Key insights

1. AI explainability operates as an architectural and governance capability rather than a technical feature appended at the end of model development.p.1
2. SR 11 7 provides a pivotal model risk management reference requiring conceptual soundness, implementation accuracy, and output reasonableness.p.2
3. High-stakes applications such as credit scoring are prioritized for explainability structures because of their regulatory oversight and consumer impacts.p.4
4. Bugni Labs established an AI-native approach that incorporates SHAP and LIME explanation tools during the model integration software phase.p.4
5. Event-driven architectures allow UK neobank platforms to preserve decision trails and escalate complex edge cases to human underwriters.p.4
6. Domain-focused microservices isolate explanation tools from primary model serving, facilitating independent scaling and version control.p.5
7. Relying solely on post-hoc explanation methods presents risks as plausible explanations may not reflect true underlying computational processes.p.6