Consumer banks and credit unions are moving from conventional dashboards and rules-based automation toward AI agents that can query data warehouses, summarize portfolio signals, monitor exceptions, draft analytical narratives and support frontline and risk teams. The value of these systems depends less on the novelty of the model than on whether the underlying data assets are governed, classified, traceable, timely and fit for regulated decision support. This article develops a practical data-stewardship and data-quality framework for governed AI agents in consumer banking, with emphasis on credit-card and deposit analytics. The framework integrates three bodies of practice: data governance and data-quality scholarship, model-risk and AI-risk governance, and banking-specific expectations around adverse-action explainability, risk-data aggregation and operational resilience. A synthetic consumer-banking test bed is used to demonstrate a risk-weighted data-quality score, business-glossary coverage, data-quality rule monitoring, lineage completeness, access-classification discipline, and AI-agent output controls. Heat-map analysis shows that the highest residual-risk areas are lineage gaps, weak glossary coverage, disputes/chargebacks, credit-bureau attributes and unexplained agent responses. The simulated before-and-after analysis indicates that a governed control layer can raise risk-weighted data-quality scores, improve agent acceptance, reduce escalation and shorten exception aging when controls are implemented as operating mechanisms rather than policy documents. The article contributes a reusable operating model for credit unions, community banks and consumer-banking teams seeking to operationalize AI agents without weakening data accountability, consumer-protection obligations or executive decision reliability.
Mudzingwa et al. (Mon,) studied this question.