A collaborative article by Chartis Research and Silent Eight

Jump to: Adequate and explainable controls | Risk appetite alignment | Conclusion

Regulatory scrutiny in a complex world

Firms face significant scrutiny from regulators in their use of artificial intelligence (AI) in financial crime compliance (FCC), largely due to concerns around the explainability and control of their models. Traditional compliance frameworks require clear justification for decisions related to risk assessment, transaction monitoring and customer due diligence (CDD). However, some AI models can operate as ‘black boxes,’ making it difficult to trace specific outputs back to defined rules or risk policies.

Compounding these concerns is a lack of clear guidelines on firms’ liability for AI-driven decisions. When a system makes a mistake – such as failing to flag a high-risk transaction or wrongly categorizing a legitimate one – there is uncertainty over who holds responsibility: the technology provider, the deploying institution, or individual compliance officers.

To address these challenges, firms are implementing governance frameworks, ensuring transparency in AI operations, and maintaining human oversight. Regulatory questions around controls, data quality and explainability remain at the forefront. Organizations must ask:

• Have adequate controls and oversight been implemented for AI tools?
• Are outcomes explainable and transparent?
• How are results calibrated and reviewed?
• Does the technology operate within the institution’s risk appetite?
• Is governance in place for data quality and model risk management?

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Adequate and explainable controls

A financial institution must demonstrate a clear understanding of its regulatory risks and the relevant policies, processes and controls it is employing to mitigate them. AI-based solutions bring significant benefits to financial crime compliance by enhancing the effectiveness and efficiency of control systems. In practice, AI encompasses various tools, including rule-based systems, natural language processing (NLP) and deep learning models. Financial institutions can set screening parameters using these tools, then apply machine learning (ML) techniques to refine the system through feedback loops informed by policy changes and operational insights.

AI solutions address longstanding challenges by providing greater accuracy and transparency. For example, Agentic AI solutions can often outperform traditional methods in predictive accuracy and decision-making by leveraging advanced models and training on large datasets. By escalating ambiguous cases to analysts with detailed explanations, they can support transparency while enabling continuous improvement by learning from resolved cases. This adaptability allows financial institutions to direct their resources toward genuine risks, aligning with risk-based approaches (RBAs) and evolving regulatory requirements.

In contrast, legacy systems often rely on outdated algorithms that generate probabilities instead of definitive decisions, leaving room for error. Without auditable explanations for their outputs, this makes tracing or justifying decisions, or pinpointing new risks, difficult.

Synchronization between technology and model governance is essential. Financial institutions should look toward AI solutions that enable business users to manage model parameters independently, ensuring flexibility and transparency. No-code or low-code interfaces are valuable in this context, as they allow non-technical users to adjust model operations quickly without relying on IT teams. This improves operational agility while fostering better user understanding of AI-driven processes.

On the governance side, ensuring that the financial institution’s model risk management (MRM) frameworks are reflected in the AI solution is critical. Adopting products that utilize explainable AI (XAI) ensures that analysts have a clear view of model behavior, can identify inaccuracies, and can assess the risks posed by erroneous outputs. By integrating advanced technological tools with strong governance practices, institutions can build compliance programs with AI systems that are efficient and effective.

Transparency and auditability are central to these solutions. Advanced systems provide detailed audit trails, logging every action taken to ensure that decision-making processes can be fully traced and understood. This allows institutions to demonstrate regulatory compliance during audits and maintain accountability in their operations. Feedback mechanisms further enhance these capabilities, enabling continuous learning and alignment with evolving regulatory requirements and risk profiles.

Finally, financial institutions need to articulate clearly to regulators the reasons behind their decision-making. This requires supporting documentation that explains what the solution has done and why, including how models are updated and aligned with specific policies. For alert adjudication or case investigations, for example, decisions must be traceable to the precise version of the model and the standards referenced. These features ensure that AI systems not only enhance compliance but also build trust with regulators and stakeholders alike.

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Risk appetite alignment

Effective AI solutions for financial crime compliance must operate within the boundaries of an institution’s risk appetite, which is shaped by carefully designed policies and risk management strategies. Rule-based systems, augmented by NLP tools, enable automated decision-making based on predefined policies, while maintaining the oversight and traceability necessary for regulatory compliance.

Continuous learning features are critical in ensuring that these systems evolve alongside emerging risks and changing regulations. By incorporating feedback from analysts and operational insights, AI systems can dynamically adjust to maintain alignment with an organization’s risk framework. This adaptability reduces the burden of manual updates while enhancing responsiveness to new threats.

Simulation capabilities further bolster this alignment. Institutions can test potential rule changes in a controlled environment, refining policies and understanding their impact on risk tolerances before deploying them in a live setting. This approach allows firms to strike a balance between innovation and caution, ensuring that automation operates within their risk appetite while remaining transparent and accountable.

Ultimately, AI solutions must empower organizations to maintain control over decision-making while ensuring that automated processes operate within the parameters of their unique compliance frameworks. This ensures not only alignment with institutional risk appetites but also a proactive stance in addressing evolving financial crime threats.

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Conclusion: AI use is a dialogue

AI holds immense promise for tackling the complexities of financial crime compliance. However, firms must ensure that their AI tools satisfy regulatory requirements by demonstrating clear controls, transparency and adaptability. Regulators, for their part, recognize the benefits that AI can bring and continue to develop their understanding of how it can be used safely. Ultimately, they need to see that financial institutions – not AI solutions – are in control of managing financial crime risk.

To achieve this, firms must focus on deploying AI solutions that align with institutional policies, empower business users to manage model parameters, and provide robust features such as explainability, audit trails and continuous learning. By keeping business users in the loop, these tools ensure operational flexibility while enabling compliance teams to address evolving regulatory standards effectively.

CEOs play a pivotal role in fostering ongoing dialogue with regulators about integrating AI into the financial sector to enhance compliance measures. Through proactive engagement, they can advocate for the responsible use of AI technologies to streamline compliance processes, mitigate risks and ensure adherence to regulatory standards. This approach demonstrates a commitment to innovation while promoting transparency and collaboration between financial institutions and regulatory bodies.

By sustaining this dialogue and focusing on features that address regulatory expectations, financial institutions can leverage AI’s transformative potential to enhance their compliance efforts. This ensures that firms not only navigate the complexities of financial crime but also uphold the integrity and stability of the financial system.