Explainable AI in Compliance: Strengthening AML Defences

Artificial intelligence is transforming how financial institutions manage compliance. Tasks like onboarding, screening, and transaction monitoring are increasingly handled by machine learning models that deliver faster, more consistent results. But with this automation comes a clear expectation from regulators, auditors, and customers: institutions must be able to explain how their AI systems work. That is why explainability has become a core requirement in financial crime compliance.

What Explainability Means

AI explainability refers to the ability to clearly describe how a model reached its conclusion. It allows compliance teams to understand why a transaction was flagged or how a risk score was determined. This visibility supports auditing, dispute resolution, and continuous improvement.

A 2024 study on ResearchGate found that explainable AI in fraud detection systems significantly reduced false positives and improved analyst efficiency. Separately, the widely cited paper by Doshi-Velez and Kim argues that explainability is essential for meeting legal and ethical requirements such as GDPR Article 22, which protects individuals from decisions made solely by opaque automated systems.

Regulatory Focus

In a joint 2022 discussion paper, the FCA and Bank of England warned that black-box models used in financial services could undermine fairness and accountability if they lack transparency. The paper recommends that AI systems be auditable and interpretable by design.

The Financial Action Task Force (FATF) also emphasises that digital transformation in AML must include human understanding and governance of machine-led processes. Meanwhile, the EU AI Act is expected to require explainability, documentation, and oversight for any AI system considered “high risk,” including those used in financial compliance.

Benefits for Fintechs and Payment Providers

Explainability is particularly useful for fintechs, neobanks, and payments firms navigating regulatory growth and operational scale. It enables:

• Transparent decision-making that enhances customer trust

• Better internal alignment across compliance, legal, and engineering teams

• Easier resolution of disputes and flagged transactions

• Stronger positioning with regulators and partners during licensing or audits

These advantages can significantly improve readiness and resilience. As outlined in this overview of strategies to operationalise explainability, embedding transparency into AI models also improves downstream efficiency and makes compliance operations more responsive. 

Governance

Traditional banks are also applying explainability frameworks. Barclays, in its submission to the UK Centre for Data Ethics and Innovation, detailed how it ensures AI models are documented, reviewed for bias, and subject to human override. This layered oversight approach is fast becoming industry standard.

How to Integrate Explainability

Institutions do not need to rebuild their AI infrastructure to meet explainability expectations. Common steps include:

• Choosing interpretable model types for core screening functions

• Applying tools like SHAP or LIME to explain complex models

• Logging and visualising decision rationale in case management platforms

• Documenting threshold logic and model validation results

• Training operational teams to understand model outputs

The consequences of not doing this, including regulatory risk and loss of operational confidence, are detailed in this analysis of the risks tied to opaque systems, which examines how lack of transparency can weaken both oversight and performance.

Policy and Research

The UK’s Digital Regulation Cooperation Forum has issued guidance on algorithmic audits and accountability. International frameworks from the OECD and BIS continue to identify explainability as a core principle for trustworthy AI.

Academic findings support these frameworks. A recent IEEE paper on AML transaction monitoring found that applying explainability tools improved investigator confidence without reducing model performance. Similarly, an arXiv study demonstrated that SHAP-based explainability can work in near real-time use cases, providing transparency in high-throughput screening environments.

Conclusion

Explainability is now a requirement for institutions deploying AI in financial crime compliance. It supports better governance, reduces risk, and improves transparency for customers, regulators, and internal teams.

Firms that treat explainability as part of core compliance, not just as a technical add-on, will be better equipped to meet regulatory expectations and build trusted, future-ready platforms.