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AI has become an essential part of modern banking operations, from strengthening screening and monitoring, to streamlining customer due diligence. But as its use expands, regulators are placing greater scrutiny on how AI is governed, deployed, and monitored. The regulatory expectations from around the world are clear: innovation cannot come at the expense of transparency, explainability, or operational resilience.
In this blog, we explore how banks can responsibly implement AI in line with regulatory expectations — and why success depends not just on technology, but on governance, culture, and risk management.
Importance of governance and third-party risks
Regulators are increasingly focused on whether AI systems are fit-for-purpose, explainable, and resilient — particularly in critical areas like anti-money laundering (AML). That means banks must adopt a compliance-first approach to AI governance. This means designing and deploying AI technologies that prioritise regulatory requirements, transparency, and auditability from the outset to ensure trustworthy, explainable, and effective financial crime compliance.
This starts with model defensibility. Banks should avoid exposing editable or opaque AI models in production environments. Instead, all models should be pre-audited, locked down, and tuned for the specific risk scenarios they are meant to detect — whether sanctions, PEPs, or transaction anomalies. Explainability must go beyond visuals for data scientists and instead empower analysts with traceability to source data, sentence-level justifications, and intuitive metrics like lift curves.
Third-party risk is another area of concern. Many institutions rely on external vendors or cloud-based AI services. Regulatory expectations now include having robust vendor selection, monitoring, and exit strategies, especially where critical operations are outsourced. Banks must ensure their vendors provide built-in model validation tools, and ideally, solutions that don’t require internal data science resources to operate safely.
Learn more: Implementing AI in AML for small FIs – risks and barriers
Deploying AI safely: where to start
AI’s promise in financial services lies in reducing the burden of repetitive tasks, improving accuracy, and freeing up resources to tackle true risk. But regulators are alert to the risk of automating poor processes turning inefficiencies into systemic vulnerabilities. Financial institutions need to have strategic planning and assessment on how to harness AI responsibly.
A safer starting point is targeted deployment in areas with strong data foundations. For example, in name and payment screening, rather than relying solely on AI to auto-close alerts, banks can reduce false positives more effectively by improving matching logic, using multi-configuration screening for different risk types (e.g. sanctions vs PEPs), and incorporating richer, risk-based data sources.
Institutions should also factor in the operational risk of AI failure. This means incorporating AI into risk-based assessments, scenario planning, and incident recovery frameworks. For example:
- Model risk management should include robust validation, drift detection, and scenario testing.
- Continuous monitoring is key to spotting anomalies and escalating issues before they impact customers.
- Human-in-the-loop oversight must remain a foundational principle — especially for abnormal events or decisions that carry regulatory implications.
The most resilient institutions are already simulating failure scenarios and using synthetic datasets to stress-test their models under adverse conditions. Regulators are exploring its viability more and more; recently as the United Kingdom’s Financial Conduct Authority announced a synthetic data partnership with Napier AI, Alan Turing Institute and Plenitude Consulting.
How to build an AI-resilient compliance culture
An AI-resilient compliance culture is one where technology, governance, and people are aligned on the goal: reducing risk and maintaining trust.
Building an AI resilient compliance structure requires:
- Clear prioritisation: AI should be used to solve foundational challenges before attempting automation. For example, improving data quality or process accuracy is more valuable and sustainable than auto-discounting alerts for efficiency alone.
- Business alignment: Success depends on how well compliance teams engage business stakeholders. Messaging around reducing false positives may not resonate but showcasing how AI can automate task allocation, improve SLA adherence, or reduce investigative lag time can.
- Transparency from day one: Auditability, traceability, and explainability must be embedded into every AI initiative, not added as a bolt-on. This mindset shift builds trust with regulators, customers, and internal teams alike.
Looking ahead, the institutions that thrive in an AI-powered regulatory landscape will be those that balance innovation with discipline. By building AI on a foundation of ethical design, operational resilience, and clear governance, banks can unlock its full potential — without compromising their obligations.
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Photo by Luke Jones on Unsplash
