Leveraging LLMs for Fincrime Operations

06 May, 2025

Financial crime prevention is one of the critical function within any fintech/banking company. In layman terms, the function is responsible for two aspects:

Protecting the customers money from fraud via social engineering, account takeovers, etc
Preventing the misuse of the firm's product/service — ensuring the company's product isn’t used for illegal activities such as money laundering, terrorism financing, etc

Fintech/banking companies use rule-based systems and ML models to flag suspicious transactions/users based on profiles, geography, and history. These flags trigger manual reviews by fincrime agents. As companies scale, fraud prevention costs grow linearly due to increasing manual review requirements.

LLMs now offer contextual understanding, cross-modal reasoning, and human-like response generation, enabling automation/enhancement of fincrime human agent workflows for increased productivity per agent.

Fincrime Workflow with LLMs

Let’s break down the fincrime agent's workflow and explore how LLMs can enhance different stages:

Fincrime Agent workflow	LLM capability	Example use-case
Alert generation	- Understanding unstructured data such as free-text in transaction descriptions or supporting documents	A user suddenly initiates a high-value transaction. Traditional ML models may flag this as suspicious. However, if the user is incorporating a new business, the transaction might be legitimate. An LLM could: Parse the transaction memo or attached documents to understand the business purpose. Verify if the receiving entity is a registered business. Evaluate supporting documents for authenticity. Automatically close the alert if the transaction is verified—eliminating the need for human investigation in these straightforward cases
Alert Investigation	Natural Language Generation: Automatically generate summaries for audit trails, SARs (Suspicious Activity Reports), and internal documentation. Contextual Understanding for ticket triage: Analyze both structured (numeric) and unstructured (textual) data to assess alert severity and recommend prioritization	Fincrime agents often check various sources such as adverse media, sanctions lists, and entity graphs to understand a transaction. An LLM can: Aggregate and summarize these inputs. Provide a narrative on the entities involved. Surface relevant risks quickly before the agent starts looking at the case. For Ex: High-priority alert: New beneficiary 'ABC Trading Ltd.' has adverse media concerns with similar entity names linked to money laundering. Potential sanctions list partial matches. Customer has no previous similar transaction patterns. Key risk factors: high-value first-time transfer to high-risk jurisdiction with negative media association
Alert disposal	Function calling capability: LLM outputs structured outputs that can trigger predefined functions in the system via an API layer, along with appropriate validation and permission controls to ensure automated decisions comply with business rules and security policies.	A retail store owner deposits $10,500 cash, triggering a SAR alert. The LLM checks that this customer has a 2-year history of similar below threshold deposits, their business type is cash-intensive, deposit patterns align with expected revenues, and finds no red flags. The system auto-closes the alert with the reasoning: "Regular business deposit consistent with merchant profile and history."
Quality Check	Natural language understanding: LLMs can understand policy documents/SOPs/regulations and break them down to actionable items with multi-layered logic. It can apply this logic to review fincrime agents decisions and flag inconsistencies in alert dispositions or breaches in SOP adherence/regulations. With a few high-quality QC examples as training input, the LLM can learn to recognize patterns through few-shot prompting, eliminating the need to hard-code every possible scenario.	A bank implements a new policy requiring enhanced due diligence for transactions over $50,000 to certain high-risk jurisdictions. Three different agents handle similar alerts: Agent A requests additional documentation and approves the transaction Agent B rejects the transaction without seeking documentation Agent C approves without requesting documentation The quality check LLM reviews these dispositions and identifies that Agent C didn't follow the new policy. It flags this inconsistency, generates a specific coaching note referencing the relevant policy section, and suggests corrective action. It also updates the quality dashboard showing policy adherence metrics. This is especially valuable when new policies are introduced but not yet fully internalized by all agents. The LLM ensures consistent application while agents are still learning the updated requirements.

Human-in-the-Loop workflow integration creates a more efficient review process. Traditionally, financial crime reviews follow a maker-checker approach where L1 analysts conduct detailed investigations and L2 reviewers validate decisions, especially for high-risk cases. With LLM integration, many L1 tasks (data gathering, summarization, initial risk assessments) can be automated, while L2 reviewers shift to verifying LLM outputs and providing feedback that continuously improves the system's accuracy and capabilities.

LLMs when thoughtfully integrated into existing human workflows can dramatically reduce manual effort, improve decision accuracy, esp. when scaling the fincrime function. The key lies in identifying the right tasks for automation, maintaining a strong human-in-the-loop process, and continuously retraining systems with real-world outcomes.