Despite jurisdictional differences, regulators share the following common priorities.

Transparency firms must clearly document how AI systems are developed and used, especially when they influence customer decisions.

Bias mitigation regulators are concerned with both direct and indirect discrimination.

Models must be tested for fairness across protected groups like gender or ethnicity.

Accountability, even when decisions are automated, responsibility remains with the institution.

Governance frameworks must specify who is responsible for monitoring, reviewing and explaining AI decisions explainability, perhaps the most difficult challenge is ensuring that customers, regulators, and internal stakeholders can understand how a model reached a decision.

This particularly important in financial services where outcomes affect access to essential financial products.