Financial institutions channel more than $190 billion annually into anti-money laundering (AML), sanctions and fraud controls, yet enforcement penalties and regulatory scrutiny continue to escalate. A key driver of this paradox is the persistent myth that “AI” is a single, catch-all technology. This linguistic shortcut, common in boardrooms and vendor pitches alike, leads to grave misapplications and erodes both operational effectiveness and regulatory credibility.
The consequences of treating all AI as interchangeable are real. Regulators and industry experts warn that misapplying generative AI models for real-time fraud detection can increase latency, false positives, and compliance risks. The OCC and others have emphasized that failing to distinguish between AI types undermines both effectiveness and regulatory trust. AI is not monolithic, and treating it that way undermines both the effectiveness and trustworthiness of financial crime programs.
Aligning ML and LLM Systems to Their Data Domains
The failure above illustrates a deeper truth: different AI families are built for fundamentally different data universes and operational tempos. Statistical Machine Learning (ML) specializes in structured, numeric, or categorical data such as transaction amounts, timestamps, or merchant codes. These models excel at delivering calibrated risk scores and anomaly alerts in milliseconds, making them indispensable for high-speed transaction monitoring, sanctions screening and fraud detection.
Generative Large Language Models (LLMs), on the other hand, are designed for unstructured, narrative-rich artifacts such as emails, PDF contracts, shipping manifests and case notes. LLMs excel at quickly extracting context, assembling narratives and mapping entity relationships, delivering outputs like SAR drafts, ownership trees and document summaries. Where ML models stumble at prose or context, LLMs falter under the sub-100 millisecond latency demands of real-time fraud detection.
The most resilient financial crime workflows now use ML for alert generation and LLMs for evidence assembly and escalation. For example, an ML model might flag a trade transaction as anomalous based on price or volume patterns. The case is then routed to an LLM, which parses supporting documents, extracts beneficial-ownership details and drafts a SAR narrative for investigator review. This handoff ensures that structured data is processed with statistical rigor, while unstructured evidence is synthesized with narrative intelligence.
Validation and monitoring regimes must also diverge. ML models are governed by tools that illuminate model health and calibration, such as Receiver Operating Characteristic (ROC) curves, feature-drift dashboards and statistical back-testing. LLMs, in contrast, require prompt inventories, citation lineage tracing and human-in-the-loop review to guard against hallucination, bias and context loss. Validation for each model is tailored to its unique failure modes. For example, statistical tests cannot catch a hallucinated vessel name in a SAR and prompt audits cannot measure the predictive power of transaction features.
Feedback loops close the system. Investigator outcomes such as SAR approvals or dismissals feed back into ML model thresholds, refining future alert precision. Meanwhile, investigator edits to LLM-generated narratives improve prompt engineering and retrieval logic, raising the quality of future outputs. By placing each engine where it wins, institutions maximize detection accuracy, minimize false positives and streamline compliance reporting.
Why One Framework Can’t Police Both
Understanding that ML and LLMs require different governance is crucial, but many financial institutions still route both through a single model-risk function designed for quantitative models. This legacy approach works for ML, where health is measured with metrics like “accuracy” and “drift” to check if the model’s predictions remain reliable as data changes over time. Standard statistical metrics, such as the Gini coefficient and KS (Kolmogorov–Smirnov) score, are used to gauge how well an ML model separates risky from non-risky transactions. However, these are technical tools that don’t capture the unique risks of LLMs.
LLMs introduce new governance requirements because, unlike ML models, LLMs can generate unpredictable or inconsistent responses, especially when exposed to new or ambiguous inputs. This unpredictability can lead to compliance failures when narratives and decisions are not explainable and auditable. For financial institutions, it is essential that every LLM-driven decision, from SAR drafts to compliance summaries, can be traced back to its data sources and logic, providing a clear audit trail for both internal oversight and external regulators.
Explainability is a regulatory and operational necessity. Financial institutions must be able to demonstrate why and how an LLM reached a particular conclusion, especially in sensitive areas like fraud investigations or regulatory filings. This often means supplementing LLM outputs with detailed citations, transparent reasoning steps and clear documentation of the data and prompts used. Auditability requires maintaining comprehensive logs of model decisions, changes and user interactions, so that every output can be reviewed and justified after the fact.
Human-in-the-loop controls are non-negotiable for high-risk outputs. Before any LLM-generated SAR narrative or compliance report is finalized, a qualified investigator or compliance officer must review, validate and approve the content. This reduces the risk of hallucinations or factual errors and ensures that the institution remains compliant with regulatory expectations for oversight and accountability.
Recent regulatory reviews have highlighted the pitfalls of applying a one-size-fits-all governance model. For example, the American Bankers Association has noted that many recent enforcement actions stem from “unmanaged innovation risk,” where banks deploy AI-driven solutions without adapting their oversight frameworks to the unique risks involved. This often results in operational failures or compliance gaps, such as overlooking narrative accuracy in generative AI outputs or applying irrelevant statistical tests to LLMs, which ultimately delay the detection of genuine risks and invite regulatory scrutiny.
These patterns reinforce the ABA’s concerns and align with recent OCC guidance, which acknowledges that while traditional model risk management principles remain necessary, they must be adapted to address the unique challenges posed by generative AI systems.
The agency’s 2025 guidance urges banks to adopt risk-based, technology-specific oversight frameworks that address the unique risks of both statistical and generative AI, including explainability, auditability and human review for LLMs.
Why Financial Crime Demands Distinct Oversight for ML and LLMs
Within financial services, ML governance is a well-oiled machine. Decades of regulatory guidance have shaped a robust framework for managing the risks of statistical models. Today, most financial institutions follow industry-standard practices, including:
- Transparent validation. Accuracy, precision-recall, and drift analysis keep models reliable as data and fraud patterns evolve.
- Explainability and documentation. Institutions must show, in plain language, how a model arrived at every score, backed by audit trails and regular reviews.
- Operational controls. Challenger models, back-testing and scenario stress tests catch errors long before they hit production.
- Full auditability. Every model change, data input and decision is logged for internal risk teams and examiners alike.
This mature governance environment has enabled ML to be widely adopted for transaction monitoring, fraud detection and sanctions screening, where structured data and statistical rigor are paramount.
Building the Case for Split-Lane Governance
Generative AI LLMs introduce a fundamentally different risk landscape that established ML governance frameworks cannot fully address. They can hallucinate facts, propagate bias, or generate outputs that are difficult to explain or audit, creating a risk profile that is especially acute in high-stakes domains like SAR drafting or regulatory communications.
A split-lane governance approach aligns oversight with those unique risks:
- Narrative-level explainability. Every assertion in a narrative must be traceable to its source, with transparent reasoning steps and clear documentation.
- Granular audit logs. Prompts, data sources, model versions, and user interactions are ensured, allowing every output to be reviewed and justified after the fact.
- Human-in-the-loop controls. No high-risk LLM-generated output, especially a SAR narrative, should be finalized without review and sign-off by a qualified investigator or compliance officer.
- Adversarial testing and prompt inventories. By surfacing risks like hallucination, prompt injection and privacy leakage before they reach production, governance takes a step forward.
Regulatory signals are moving in this direction. The OCC’s 2025 GenAI principles, for example, explicitly recognize that while traditional model risk controls are necessary, they are not sufficient for generative AI. Examiners are beginning to expect separate documentation and assurance packs for ML and LLM systems, reflecting the growing consensus that one-size-fits-all governance is inadequate for today’s AI landscape.
The Path Forward for Financial Crime AI Governance
Financial institutions face a critical juncture in their fight against financial crime.
By establishing dedicated oversight tracks tailored to the statistical rigor of ML and the narrative complexity of LLMs, institutions clarify accountability, accelerate issue resolution and build lasting trust with regulators and stakeholders. As LLMs become more deeply embedded in compliance workflows, the case for specialized, risk-specific governance will only grow stronger.
The future of financial crime prevention depends on institutions’ willingness to innovate beyond legacy frameworks and adopt governance models that align oversight with each AI model’s distinct risk profile and operational value. Separating them while uniting the mission is the clearest path to cutting false positives, improving efficiency and satisfying increasingly AI-savvy regulators.
