AI Research Summary
Before Human ReviewBased on abstract · Full text not reviewed
This source is an abstract-based summary examining the challenges of AI governance in banking, and the full text was not reviewed. The authors argue that as AI spreads to credit evaluation, fraud detection, and risk management, issues of transparency, accountability, and model risk are becoming more serious. Explainability is presented not as a mere technical property but as a governance capability for documenting, reconstructing, verifying, and monitoring decisions. It also emphasizes an approach that combines lifecycle governance, independent validation, data lineage, documentation, monitoring, executive accountability, and human oversight.
Key Points from the Abstract
- Explainability is defined as a governance capability.
- Post hoc explanations, the trade-off between performance and interpretability, and the need for stakeholder-specific explanations are presented as issues.
- The integration of regulatory alignment and traditional risk management is considered necessary.
Relevance to AI Law and Policy
It provides direct implications for the design of explainability, model validation, accountability, and integrated risk governance in bank AI.
Limitations to Consider
- It was written only from the abstract, so the detailed methods and evidence cannot be confirmed.
- No limitations are explicitly stated.
- Legal effects or empirical results cannot be determined from the abstract alone.
Please compare with the original text before citing or using it in your assessment.
Abstract Preview
The adoption of AI in the banking sector has made its way into various functions…Read more in the abstract and original text Research Topic
Original text and source
The Architecture of AI Governance in Banking: Bridging Compliance and Explainability Gaps
The Korean title on this page was translated by AI.- Database
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