The largest single cost driver. Compliance staff, AI risk officers, cross-functional committees, and human-in-the-loop oversight processes make up the backbone of AI governance spending. 

Producing and maintaining technical files, data lineage reports, fairness assessments, and operational logs – an ongoing effort essential for audits and regulatory inspections. 

From robustness checks to fairness audits, banks must rigorously validate AI models before and during use. This includes bias detection, cybersecurity stress testing, and independent validation. 

Continuous monitoring of AI outputs, detecting data drift, handling incidents, and providing updated staff training – ensuring compliance is not a one-off exercise. 

Investments in secure and auditable data pipelines, governance technology platforms, and scalable compute/storage solutions to support compliance demands. 

Initial conformity assessments, internal Quality Management Systems, and ongoing audits by regulators or third parties – all adding to the lifecycle cost of AI compliance.