Artificial intelligence is now core to many banking functions from fraud detection to customer servicing and software development. A fundamental mismatch exists between how advanced AI systems operate and how financial regulation expects accountability and traceability.
The unraveling of traditional assurance
Regulators and auditors rely on backward-traceable decision chains and deterministic controls. Contemporary AI models, especially large language models, operate by compressing vast training signals into parameter patterns. That compression is non-invertible. There is no simple, step-by-step provenance from input to output the way rule engines produce one. The result is a technical barrier to classical QA and forensic expectations: post hoc explanations may be plausible but not provably causal.
Compression versus traceability
Compression means knowledge is encoded in distributed weights rather than discrete rules. Attempts to force full traceability confuse statistical attribution with legal responsibility. Banks cannot pretend black box outputs are auditable in the same way as coded logic. Assurance must therefore shift from perfect explanation to measurable, repeatable performance evidence.
Accelerating risks and evolving compliance demands
New threat vectors
AI increases speed and scale for attackers. Personalized social engineering, automated vulnerability discovery, synthetic identity creation, and model poisoning are immediate concerns. Third party models and data pipelines widen the attack surface. Malicious injection of training data can bias models or create exploitable behaviors. These threats amplify operational and reputational loss velocity.
Regulatory push for continuous oversight
Policy is reacting. The EU AI Act uses a risk-based approach that imposes documentation, conformity checks, and human oversight for high-risk systems. DORA requires continuous ICT resilience and testing across the lifecycle. Together they move compliance from pre-deployment signoff to continuous assurance and demonstrable runtime reliability.
Forging a practical path for AI assurance
Banks need regulatory clarity that reflects compression dynamics and probabilistic outputs. Effective governance will combine outcome-focused standards, continuous testing (drift detection, red teaming, adversarial checks), strict vendor controls, and clear accountability chains. That approach lets institutions adopt powerful AI while providing regulators with reproducible evidence of performance and risk control.
Senior executives must engage regulators, invest in persistent assurance tooling, and adapt governance to a model-centric world. This is a strategic challenge that will define operational resilience and trust in finance for years to come.
