Why governance literacy is the new core AI skill

In banking, a high-performing model that cannot withstand an audit becomes a risk. The requirement for auditability extends to how models are built, with clear documentation, traceability, and control over decision-making. Within this context, AI systems operate under regulatory scrutiny and directly influence customer outcomes, making technical capability alone insufficient.

At Mashreq, governance literacy is a prerequisite for working on production AI systems. Experimentation and deployment are fundamentally different environments. A prototype that performs well in a sandbox cannot move into production unless it is auditable, monitored for model drift, secured against misuse, documented rigorously, and defensible under regulatory review.

Meeting these requirements depends on institutional discipline. Through the Enterprise Data and AI Academy, responsible AI principles are embedded alongside technical training. Practitioners develop capabilities in fairness assessment, validation rigor, explainability standards, data privacy controls, and ongoing monitoring. They are also trained to communicate effectively with risk, compliance, and business teams whose decisions depend on these systems.

This reframes what AI expertise means in financial services. The objective is not model complexity, but operational durability; systems that regulators can review, teams can manage, and customers can trust.

Xi Liang, Head of AI, Mashreq