Governing the Ungovernable: Why AI Regulation is Becoming a QA Issue in Banking
This article is the first installment in a three-part series examining AI governance in banking quality assurance (QA) and software testing. It explores the struggles financial institutions face in reconciling modern AI systems with regulatory expectations, the responses from global regulators, and the pivotal role QA teams play in AI risk, assurance, and accountability.
Why AI Governance Has Landed on the QA Desk
Artificial intelligence has transitioned from a peripheral technology to a core component of financial services. It now shapes crucial banking functions such as fraud detection, transaction monitoring, credit decisioning, surveillance, and claims handling. AI-driven test automation and synthetic data generation are now integral to quality engineering pipelines.
This evolution has rendered governance an operational problem for QA and software testing teams. The crux of the issue lies in a significant mismatch between regulatory expectations and the behavior of modern AI systems.
Jennifer J.K., a director of AI Risk & Governance, argues that regulatory requirements and the technology are fundamentally incompatible. Traditional regulatory frameworks assume that decisions can be traced through clear, invertible processes. However, this assumption collapses when AI is introduced. Modern AI models operate through data compression, making it impossible to reconstruct original reasoning as regulators expect.
The Testing Problem
This leads to a pressing testing problem: regulators increasingly demand explanations for AI-driven decisions. When asked for evidence chains, the typical response is that the system has compressed that information away. This creates gaps in accountability, which QA teams must address, as testing is where evidence should reside.
J.K. emphasizes that this issue cannot be resolved through better documentation alone; it requires regulatory frameworks that understand the complexities of AI systems.
AI Risk Accelerates the Governance Problem
The urgency of this mismatch is intensified by how AI is reshaping the threat landscape. AI is not merely a defensive tool; it is also accelerating cyber risk. Attackers now utilize advanced tools to conduct highly personalized attacks, resulting in significant vulnerabilities for banks.
David Cass, a cybersecurity instructor, notes that organizations can lose substantial amounts of money rapidly due to AI-driven incidents. Testing teams are thus required to validate systems under extreme time pressure, ensuring safety and integrity.
Furthermore, vendors pose a major risk vector, with many attacks entering through third-party software. QA teams must now validate data integrity, model behavior, and system responses to adversarial inputs to mitigate these risks.
Regulation Meets QA Reality
The intersection of AI capabilities and regulatory requirements is becoming increasingly complex. The EU AI Act introduces a risk-based framework for many AI use cases in financial services, demanding governance, transparency, and post-deployment monitoring.
Consequently, testing is no longer confined to pre-release validation; it must be a continuous requirement. The World Economic Forum has pointed out that AI governance failures often arise after deployment, as systems adapt and interact with each other.
Why Banks are Craving Governance Clarity
Financial institutions are not resisting regulation; they seek clarity that aligns with technical realities. Without it, AI initiatives are likely to stall or become compliance risks. The inability to provide the evidence chains that regulators demand places QA teams in an untenable situation, emphasizing the need for governance in quality assurance.
As AI becomes integral to banking operations, the role of QA is evolving from simple defect detection to enforcing governance. In the subsequent parts of this series, we will delve into how regulators in Europe, the UK, and Asia are addressing these challenges in AI governance.
