Green Sheet interviews LexisNexis Risk Solutions' Camilla Yellets

As regulators increasingly ask whether AML programs are effectively identifying and mitigating financial crime risk, banks and payments providers face new questions about technology, data quality, information sharing and how they allocate compliance resources. Green Sheet spoke with Camilla Yellets, director of financial crime compliance at LexisNexis Risk Solutions, about what is driving the shift to outcomes-based compliance, how institutions may be evaluated differently and what they can do now to prepare.

Green Sheet: What is driving the shift from process-based to outcomes-based AML compliance?

Camilla Yellets: The shift toward outcomes-based AML compliance has been building for several years, and the recent FinCEN proposal provides one of the clearest indications of where regulators want the industry to go.

Historically, AML programs were often evaluated based on whether required processes were in place and consistently followed. Today, regulators are increasingly focused on a different question: Is the program effectively identifying and mitigating meaningful financial crime risk?

Several factors are driving this evolution. Financial crime has become more sophisticated, digitally enabled and interconnected. Criminal networks move quickly, exploit multiple institutions simultaneously and often operate across fraud, cybercrime, money laundering and sanctions evasion schemes. Traditional compliance processes alone are often insufficient to keep pace.

At the same time, regulators have encouraged responsible innovation. The OCC, Federal Reserve, FDIC, FinCEN and NCUA have repeatedly emphasized the importance of adopting technologies and analytical approaches that improve AML effectiveness.

Regulators are also modernizing requirements to improve customer experience while maintaining strong risk management. For example, allowing alternative methods for obtaining taxpayer identification information during account opening reflects a recognition that institutions can reduce friction while maintaining confidence in customer identity verification and risk assessment.

Ultimately, the industry is moving beyond demonstrating that a process exists and toward demonstrating that the process produces meaningful risk outcomes.

GS: How will financial institutions be judged differently under an outcomes-based approach?

CY: Under an outcomes-based framework, regulators are expected to evaluate AML programs more holistically. Rather than focusing primarily on whether every procedural step was followed, greater emphasis will be placed on whether an institution's AML program is effective, risk based and reasonably designed to identify and address illicit finance risk.

The proposed framework creates an important distinction between how a program is designed and how it is maintained. This helps shift the conversation away from isolated procedural deficiencies and toward whether the institution has established a sound program aligned with its risk profile.

This does not signal lower regulatory expectations. Rather, institutions will need to better justify their decisions, demonstrate how resources are directed toward higher-risk areas and show that controls support effective financial crime risk management.

Successful programs are likely to be measured increasingly by their ability to identify higher-risk activity, support law enforcement objectives and generate actionable intelligence, rather than by the volume of alerts reviewed or documentation produced.

GS: Why isn't AI alone enough to improve AML effectiveness?

CY: AI can be a powerful enabler, but it is not a substitute for a well-designed AML program.

The effectiveness of any AI model depends on the quality of the data, the strength of governance and the clarity of the underlying risk framework. If an institution does not understand its risk profile, maintain reliable customer information or implement appropriate controls and oversight, AI may simply automate existing weaknesses.

The most effective institutions view AI as one component of a broader financial crime strategy. Technology can help identify patterns, prioritize risk and improve operational efficiency, but institutions must still ensure meaningful risks are detected, investigated and addressed appropriately.

In other words, AI can help accelerate outcomes, but it cannot define them. The risk framework and governance structure must come first.

GS: What role do data quality and information sharing play in identifying financial crime earlier?

CY: Data quality is foundational to effective AML outcomes. Institutions cannot accurately assess customer risk, identify suspicious activity or deploy advanced analytics when underlying data is inaccurate, incomplete, duplicated or outdated. Poor data quality can directly affect risk assessments, investigations and decision-making.

Strong AML programs require institutions to continuously maintain, enrich and connect customer information throughout the customer lifecycle, including onboarding, ongoing due diligence, transaction monitoring, sanctions screening and investigations. Information sharing is equally important. Financial crime rarely occurs within a single institution. Criminal networks frequently operate across multiple banks, payment providers, channels and geographies. As a result, industry consortiums, public-private partnerships and collaboration among institutions are becoming increasingly valuable sources of intelligence.

Institutions that are best positioned to identify emerging threats typically combine strong internal data management with external intelligence and collaborative information-sharing frameworks.

GS: How can financial institutions better detect increasingly sophisticated criminal networks?

CY: Collaboration remains one of the most effective strategies. Criminal organizations do not operate in silos, yet many institutions still do. Fraud, AML, cyber, information security and operational risk teams often hold different pieces of the same risk picture. Connecting those insights can provide a more comprehensive view of potential criminal activity.

Organizations should also leverage consortium intelligence, government advisories, law enforcement guidance and emerging typology reporting. Criminal tactics evolve rapidly, and institutions benefit from visibility beyond their own customer base.

Equally important is fostering trust among customers and employees by providing accessible channels to report suspicious activity. Human intelligence remains a valuable component of financial crime detection.

Success increasingly depends on an institution's ability to combine internal collaboration, external intelligence, advanced analytics and continuous education.

GS: What practical steps should banks and payments providers take now to prepare for this shift?

CY: First, institutions should evaluate whether their AML program is aligned to risk outcomes or focused primarily on compliance activities. This starts with reviewing the enterprise AML risk assessment, identifying key stakeholders across the organization and benchmarking current practices against the direction outlined in the FinCEN proposal.

Second, institutions should assess whether they have the data foundation necessary to support risk-based decision-making. Data governance, customer data management, entity resolution and ongoing customer risk monitoring will become increasingly important.

Third, organizations should strengthen collaboration across AML, fraud, cyber and business functions. As financial crime risks continue to converge, effective risk management requires cross-functional visibility. Finally, leadership engagement is critical. The shift to outcomes-based compliance is not solely a compliance initiative. It requires strategic investment, executive sponsorship, and a clear tone from leadership that effectiveness, risk intelligence and financial crime prevention are enterprise priorities.

Bottom line: The institutions most likely to succeed will be those that can clearly demonstrate an understanding of their risks, prioritize resources effectively and produce measurable financial crime outcomes, not simply those that can demonstrate adherence to a process.

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