Agentic Workflow Automation: The Next Step in AML Compliance

Money laundering is a persistent challenge for financial institutions that continues to grow in 2024, with $300 billion laundered yearly through the United States alone. In response, the demand for sophisticated compliance solutions is intensifying and Agentic Workflow Automation offers a promising approach.

This is a novel method involving the use of AI to streamline and enhance Anti-Money Laundering (AML) processes. We will learn more about this technology and how to implement it for your organization in this article.

Understanding Agentic Workflow Automation

Agentic Workflow Automation or Agent-based Workflow Automation represents a major advancement from traditional automation methods. It includes utilizing AI agents to manage complex AML tasks with minimal human intervention. 

Unlike conventional systems that rely on predefined scripts, agentic workflows employ AI agents capable of perceiving, reasoning, and acting autonomously to achieve specific goals. Its main characteristics include-

• Goal-Oriented approach: AI agents collaborate to achieve defined objectives, optimizing processes through collective intelligence.
• Adaptive: These systems learn from past experiences, adjusting to new circumstances and improving performance over time.
• Interactive: AI agents interact with each other and human users, facilitating seamless information exchange and decision-making.

The impact of agent-based workflow automation spans multiple domains, particularly in Anti-Money Laundering (AML) compliance and financial crime prevention, making them an essential tool for modern financial institutions.

How Agentic Workflows Differ from Traditional AML Methods:

Agentic workflows in AML compliance represent a significant departure from traditional methods. In conventional AML systems, automation is rule-based and relies on predefined scripts, which require human intervention for updates and adjustments. These systems often fail to adapt in real-time to changing financial crime patterns, leading to inefficiencies like high false positive rates and slow response times.

In contrast, agentic workflows utilize autonomous AI agents that can perceive, reason, and act on their own. These agents continuously learn from real-time data, allowing them to adapt dynamically to new compliance challenges without requiring manual reprogramming. This leads to greater flexibility and improved accuracy in detecting suspicious activity, reducing false positives by up to 60% while increasing fraud detection by 50%​.

Furthermore, agentic workflows incorporate large language models (LLMs), which enable agents to interpret complex regulatory documents and communicate with human users in natural language. This improves the efficiency of tasks like case summarization, regulatory reporting, and customer due diligence​. 

Overall, agentic workflows can operate with minimal human intervention, drastically enhancing the speed and accuracy of AML processes compared to traditional systems.

Applications of Agentic Workflows in Financial Services/Fincrime

AI agents are not just tools for assisting with individual tasks; they are designed to manage comprehensive multi-step processes. This makes it possible to automate more complex actions involving multiple stages in the Financial sphere, such as:

1. Credit Risk Assessment: Agentic systems can automate the preparation of credit-risk memos by gathering borrower data, calculating financial ratios, and identifying discrepancies. This process not only reduces the time taken for review cycles by up to 60% but also enhances risk management by ensuring accuracy at every stage​.
2. Loan Underwriting: In financial institutions, agentic workflows streamline loan underwriting by autonomously collecting data, analyzing it, and generating documents. Multiple agents collaborate to complete tasks, resulting in faster underwriting decisions and improved accuracy​.
3. Fraud Detection in Payments: Real-time transaction monitoring is another area where agentic workflows excel. By continuously scanning transaction data, agents detect fraud or money laundering patterns in real-time, significantly reducing potential losses​.
4. Regulatory Reporting: Agentic workflows automate the generation of compliance reports such as Suspicious Activity Reports (SARs). These systems ensure consistency by automatically updating reports as new data emerges, streamlining regulatory submissions.
5. Customer Due Diligence (CDD): AI agents can automate the customer due diligence process, assessing a client's risk profile by analyzing vast amounts of historical and behavioral data. This reduces human errors and ensures faster onboarding while complying with KYC regulations​.

AI Agents in Action: Executing End-to-end AML Workflows

Focusing on AML case management, AI agents play an important role in executing entire workflows from start to finish. By leveraging AI agents to handle repetitive and data-intensive steps, financial institutions can drastically reduce the time spent on manual AML processes while improving accuracy and compliance. Let’s explore five major areas that depict how AI agents streamline key AML workflows-

1. Case Management with AI Agents

AML case investigations involve multiple stages, from data gathering to final report submissions, and AI agents are instrumental in automating this process. In a typical AML case, an AI agent can handle the following steps:

• Case Summarization: The agent begins by summarizing the case based on available transaction data, risk indicators, and previous investigations. This eliminates the need for compliance officers to manually review extensive reports.
• Customer Research: AI agents can automatically gather and analyze customer information, including personal data, transaction histories, and potential links to suspicious activities. This allows investigators to quickly gain a comprehensive view of the customer’s profile.
• Request for Information (RFI) Creation: The agent drafts RFIs as needed, requesting additional information from external sources or internal departments. This step is crucial for cases where more details are needed to resolve red flags.
• SAR Narrative Writing: After gathering all relevant information, the agent can generate a Suspicious Activity Report (SAR) narrative. The AI uses standardized formats, ensuring that reports meet regulatory requirements while saving time on manual report writing.

By automating these steps, an AI agent streamlines the entire investigation, allowing compliance teams to focus on complex decision-making rather than administrative tasks. Solutions like Lucinity's Luci Copilot make this possible by transforming data into clear, actionable insights, enabling faster and more efficient investigations.

2. Proof of Business Checks

Another key area where AI agents can automate workflows is in proof of business checks, a key part of Know Your Customer (KYC) and AML compliance. These checks involve verifying the legitimacy of a business, and AI agents can seamlessly execute each step of this process:

• Business License Validation: The agent automatically verifies the business’s registration and license information, cross-referencing it with official government records to ensure authenticity.
• Business Address Search: AI agents can search for the business’s physical location, validating that the address exists and is legitimate. This step helps to confirm that the business operates in the location it claims.
• UBO Information Gathering: The agent identifies and gathers information about the Ultimate Beneficial Owners (UBOs) of the business, ensuring that ownership structures comply with AML regulations.
• Online Presence Check: AI agents can also assess the business’s online presence, scanning websites, social media platforms, and other digital records to ensure the company’s legitimacy.
• Summary Generation: Once all the relevant information is gathered, the agent generates a comprehensive summary of the proof of business check, highlighting any discrepancies or risks.

Automating this end-to-end process ensures that proof of business checks are completed quickly, thoroughly, and with minimal manual intervention. AI-driven workflows like this not only speed up KYC processes but also enhance accuracy by reducing the likelihood of human error.

3. KYC Checks

Know Your Customer (KYC) checks are a critical part of AML processes, ensuring that financial institutions verify the identity of their customers and assess their risk profiles. AI agents can automate the entire KYC process from start to finish:

• Customer Identity Verification: The AI agent can gather and verify personal identification documents, cross-referencing them with official databases to ensure authenticity and prevent identity fraud.
• Risk Scoring: The agent can automatically calculate a customer's risk score by analyzing factors such as transaction patterns, geographic risk, and prior history. These scores are continuously updated based on new data.
• Sanctions and Watchlist Screening: AI agents can cross-check customer profiles against global sanctions lists, PEP (Politically Exposed Persons) databases, and other watchlists to flag any risks.
• Enhanced Due Diligence (EDD): For higher-risk clients, the agent can carry out enhanced due diligence, collecting additional information, and generating a detailed report to comply with regulatory requirements.

By automating the KYC process, AI agents enable institutions to conduct thorough identity verification and risk assessment efficiently, reducing the manual burden on compliance teams while ensuring regulatory compliance.

4. Transaction Monitoring

Transaction monitoring is an essential component of AML compliance, requiring continuous observation of financial transactions to detect and report suspicious activity. AI agents can fully automate the monitoring and analysis of transactions:

• Real-Time Transaction Analysis: AI agents can monitor transactions in real time, flagging any that deviate from expected patterns or exceed risk thresholds. This reduces the time it takes to detect potential money laundering activities.
• Behavioral Pattern Analysis: The agent uses machine learning to track and analyze behavioral patterns in customer transactions. It can identify unusual activity, such as rapid transfers or transactions in high-risk areas, that might indicate money laundering.
• Alert Generation and Escalation: The AI agent generates alerts for flagged transactions and can escalate suspicious cases for further investigation, automatically including relevant data for human review.
• Automated Reporting: Once suspicious transactions are confirmed, the AI agent can generate reports required for regulatory filings, such as SARs, ensuring compliance with minimal human intervention.

This end-to-end process automates what would otherwise be a labor-intensive monitoring task, improving the speed and accuracy of detecting suspicious activities.

5. PEP and Adverse Media Screening

Screening for politically exposed persons (PEPs) and adverse media is a vital part of AML compliance, ensuring that financial institutions are not dealing with high-risk individuals. AI agents can automate this entire process:

• PEP Identification: The agent automatically scans global PEP lists to determine whether a customer or associated entity falls under this category, flagging any potential risks.
• Adverse Media Search: AI agents conduct comprehensive media searches for negative news about a customer, business, or UBO. These searches span across news websites, social media, and specialized databases to detect any adverse reports that could pose a risk.
• Risk Reporting: After identifying PEPs or adverse media, the agent generates a risk profile report, summarizing the findings and any potential compliance concerns.
• Continuous Monitoring: AI agents can continuously monitor customers for any new mentions in the media or updated sanctions lists, ensuring that compliance teams are always informed of emerging risks.

By handling this screening process from start to finish, AI agents reduce the time it takes to assess risks while ensuring comprehensive and up-to-date checks are in place​.

Leveraging Federated Learning for Agent-Based Workflow Automation

Federated learning helps improve agent-based workflow automation in AML by enabling institutions to collaborate on improving machine learning models without sharing sensitive data. It improves agents’ ability to detect financial crimes by training models on decentralized datasets across institutions. 

The models also continuously evolve by learning from broader datasets. This reduces false positives to as low as 12% while improving detection rates and also allows agents to make more informed decisions in real time​. 

As a result, federated learning ensures data privacy while boosting the accuracy of AI agents that automate compliance tasks such as transaction monitoring, suspicious activity reporting, and case management​. The steps for implementing federated learning for Agent-Based Automation are-

1. Ensure Data Privacy: Prepare your data infrastructure to support local model training while adhering to privacy laws like GDPR.
2. Choose Technology Partners: Select platforms that integrate federated learning and AI automation seamlessly, ensuring encryption and scalability.
3. Collaborate with Institutions: Partner with other financial entities to train models collectively, improving the AI agents' accuracy while maintaining data privacy.
4. Deploy and Automate: Use AI agents powered by federated models to automate AML tasks, enhancing efficiency and reducing compliance costs.
5. Monitor and Iterate: Continuously update and refine the models based on new data and performance feedback to keep improving detection accuracy​.

By combining federated learning with agent automation, institutions can create a scalable, privacy-compliant, and highly efficient AML system capable of adapting to emerging financial crime patterns.

How Lucinity Can Help

Lucinity supports agentic workflow automation through its Luci AI Copilot and the Luci Studio platform, backed by its patented federated learning technology. Let’s understand how:

Luci Copilot

The Luci Copilot integrates seamlessly into existing systems, automating various AML tasks such as case summarization, regulatory reporting, and suspicious activity report (SAR) generation.  

Utilizing OpenAI's GPT-4 and GPT-3.5 technologies, Luci can analyze 100+ page reports, summarize cases, and conduct deep searches in multiple languages. It automates tasks that typically take hours, reducing them to minutes.

This system leverages Generative Intelligence Process Automation (GIPA), which combines AI, deep learning, and robotic process automation to streamline repetitive tasks and enhance decision-making​ .

Luci Studio

The Luci Studio offers customizable workflows through a no-code interface that allows compliance teams to tailor workflows for specific needs without requiring technical expertise. This automation suite is designed to boost productivity, allowing compliance teams to automate end-to-end processes, including transaction monitoring and investigative tasks​.

Federated Learning

Luci and Luci Studio are powered by Lucinity's patented federated learning technology that allows multiple financial institutions to collaborate by training AI models across decentralized datasets - all without sharing any sensitive data. This privacy-preserving method helps improve the accuracy of AML investigations while maintaining regulatory compliance.

Summing Up

Agentic Workflow Automation is a notable advancement in AML compliance, offering enhanced efficiency, accuracy, and cost-effectiveness. It provides a powerful solution to streamline processes, reduce errors, and improve the overall effectiveness of AML efforts. Key takeaways from our discussion about this technology include:

1. Enhanced Efficiency: Automating complex tasks reduces manual workloads and improves operational efficiency, allowing compliance teams to focus on high-value activities.
2. Improved Accuracy: AI agents and LLMs enhance the accuracy of compliance operations, reducing false positives and improving detection capabilities for genuine financial crimes.
3. Cost Reduction: By automating routine tasks and improving the efficiency of investigations, Agentic Workflow Automation can significantly reduce compliance costs for financial institutions.
4. Scalability: Agentic workflows are highly scalable, adapting to changing regulatory requirements and operational demands without the need for extensive system overhauls.

To explore how Lucinity's innovative solutions can empower your compliance efforts and transform your AML processes through Agentic Workflow Automation, visit Lucinity now.