The hidden risk of AI in financial compliance: Are you prepared?

AI is transforming compliance in financial services, offering efficiency gains while introducing new risks that demand robust governance.

Artificial intelligence (AI) is no longer a futuristic concept. It is now embedded in the day-to-day operations of all financial services firms, from banks and investment managers to crypto exchanges and payments platforms. AI now plays a critical role in compliance processes, automating know your customer (KYC) checks, streamlining transaction monitoring, and identifying unusual behaviour in real time.

According to the International Data Corporation (IDC), financial institutions globally are expected to spend over £44 billion on AI systems in 2025, with banking alone accounting for approximately £20 billion. The UK financial sector is among the most active adopters in Europe, driven by both commercial benefits and regulatory encouragement. A 2024 joint survey by the Bank of England (BoE) and the Financial Conduct Authority (FCA) found that 72% of UK-regulated firms are actively using or piloting AI and machine learning tools—an increase from 67% in 2022. This includes a high concentration in anti-money laundering (AML), fraud detection, and client onboarding.

• 85% of digital-first payment firms report live AI integration, particularly in fraud analytics and real-time risk scoring.
• 60% of crypto and virtual asset providers surveyed by ESMA and the FCA reported using AI to monitor transactions and identify market abuse.
• Among investment firms, over 50% now use AI in regulatory reporting, suitability assessments, and surveillance of employee trading.

Yet, as adoption increases, so do the risks

While AI improves efficiency, speed, and scale, it also introduces new compliance risks, many of which remain under-acknowledged and insufficiently addressed. These “hidden risks” lie not just in what AI does but also in how it does it, often without adequate oversight, auditability, or transparency. The key question for firms now is not whether to use AI but how to use it responsibly.

The double-edged sword: AI’s promise and pitfalls

AI can cut costs and reduce manual errors by automating identity verification, document processing and behavioural analysis. AI-powered compliance tools can scan millions of transactions in seconds — something no human team could match.

However, the same technologies introduce critical vulnerabilities. As firms deploy advanced models like generative AI or deep learning, the logic behind decisions becomes harder to trace, a concern regulators are increasingly addressing.

The FCA, PRA, and BoE jointly issued a Discussion Paper (DP5/22) highlighting that explainability, accountability, and resilience are essential features of AI in financial services. They stress that AI tools must be aligned with the SM&CR regime, particularly in ensuring clear individual responsibility and traceable decision-making.

The hidden compliance risks lurking in AI

1. Opaque decision-making and regulatory exposure: Regulators are placing increasing emphasis on explainability. The UK’s Financial Conduct Authority (FCA) and similar bodies globally are urging firms to ensure that any AI systems used in compliance can be audited and justified. If a customer is flagged as high risk or denied service due to an AI system, firms must be able to clearly and consistently explain why that decision was made. Failure to do so can lead to accusations of unfair treatment, discrimination, or even breaches of consumer protection laws. In a compliance context, this could result in fines, reputational damage, or suspension of operations. This aligns with FCA Principles such as PRIN 2.1 Principle 6 (customers’ interests) and SYSC 15A (operational resilience).
2. Model drift and data bias: AI models rely on training data, and if that data is biased, incomplete, or outdated, the outputs can be flawed. A model that once detected fraud accurately may begin missing suspicious activity over time as patterns evolve. This phenomenon, known as model drift, is particularly dangerous in compliance, where constant vigilance is essential. Moreover, biased data sets can lead to unfair or inconsistent treatment of different customer demographics. For example, a model trained on historically skewed data might disproportionately flag transactions from certain countries or ethnic groups, potentially violating anti-discrimination regulations FCA Principles 7 (communications with clients) and 9 (customers: relationships of trust).
3. Over-reliance and the skills gap: As AI tools become more advanced, there is a temptation to hand over too much responsibility to machines. Some firms are already overly reliant on AI outputs without applying critical human judgment or adequate validation. A growing skills gap compounds this issue. Many compliance professionals lack the technical understanding to assess or challenge the outputs of these systems. This creates a dangerous scenario where AI decisions are approved without scrutiny by teams who may not fully understand the underlying logic or risks, weakening internal controls and increasing regulatory exposure. This is especially relevant under SYSC 21 (risk control), emphasising robust oversight and governance.
4. Fragmented governance and accountability: AI systems are often deployed in isolation by different departments. Without centralised oversight, this leads to fragmented governance, unclear responsibilities, and inconsistent standards. The PRA’s SS1/23 guidance emphasises the need for firms to align AI governance with existing risk management frameworks, ensuring proper escalation and audit protocols.

Why this matters more than ever

AI governance is becoming a priority area for UK regulators. In 2025, the FCA’s regulatory priorities include greater scrutiny of firms using AI in core functions, particularly where consumer outcomes are affected. The UK Government’s AI Regulation White Paper (2023) continues to guide principles-based, sector-specific oversight, and the EU Artificial Intelligence Act, expected to come into effect in 2026, will set new standards for “high-risk AI” systems used in financial services.

Internationally, the IOSCO 2024 report on AI in capital markets has urged regulators to adopt more consistent supervisory approaches. Frameworks like the OECD AI Principles and Singapore’s FEAT Principles are being used as reference points for UK policy development.

Key actions for compliance leaders

Compliance leaders must act now to prepare for the evolving risks and expectations around AI. Here are six essential steps:

1. Choose AI tools and providers with active oversight: Firms must ensure that both the AI tools and their data providers have dedicated teams responsible for maintaining, updating, and monitoring the systems and data inputs. AI solutions should not be treated as static, off-the-shelf products. Ongoing model validation, regular data updates, and clear vendor governance are essential, particularly where third-party technologies are used in regulated environments. This supports compliance with SYSC 8 (Outsourcing) and SYSC 13 (Operational Risk) in the FCA Handbook.
2. Audit AI systems: Ensure every AI tool in compliance is documented, regularly reviewed, and capable of being explained to regulators and customers.
3. Establish a governance framework: Define clear policies for AI development, deployment, and oversight. Assign ownership, implement approval processes, and establish escalation procedures.
4. Validate and monitor training data: Rigorously assess training data for fairness, representativeness, and bias. Ensure data is regularly refreshed and aligned with regulatory expectations.
5. Invest in AI literacy—upskill compliance teams: Ensure compliance staff are trained to understand and interrogate AI outputs. The ability to question, validate and challenge AI-driven decisions is essential to robust risk management and is increasingly expected by UK regulators.
6. Implement human-in-the-loop controls: For high-risk decisions, ensure human oversight remains central. AI should inform decisions, not replace human judgment.

Looking ahead

The future of compliance will be shaped not by how much AI a firm uses, but by how responsibly and transparently it is applied. As regulatory expectations evolve, firms that embed governance, fairness and accountability into their AI strategies will stand out, not just to regulators, but to clients and investors alike.

At Complyport, we embrace RegTech and AI technologies as integral to the future of compliance. We have successfully incorporated AI-driven solutions into our own internal processes, from risk assessments and monitoring to client onboarding and reporting. More importantly, we deliver these capabilities to clients across the globe, helping regulated firms adopt AI tools in a responsible, effective and regulator-ready manner.

We believe technology should enhance compliance, not compromise it. With the right oversight, controls and expertise, AI can be a powerful asset in meeting today’s challenges and tomorrow’s expectations.