5 effective AML transaction monitoring tactics

Tapan Shah, Corey Minard, Alex Ziegler
5 effective AML transaction monitoring tactics

Enhanced anti-money laundering (AML) monitoring and due diligence tactics can help increase efficiency and support regulatory compliance.

Financial criminals continually refine their tactics and employ increasingly sophisticated technology to achieve their objectives, which means that organizations have to stay on top of the latest trends and implement solutions to effectively detect suspicious activities. As they do so, many organizations are exploring how to both reduce costs and make their anti-money laundering (AML) programs more efficient.

To gain a deeper understanding of how financial crime programs are improving transaction monitoring and adapting to recent regulatory changes, we spoke with specialists at several financial services organizations that serve diverse customer bases, operate in various locations, and have unique requirements.

These conversations yielded some interesting insights, including five tactics that organizations can implement to help improve their financial crime program analytics and transaction monitoring.

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1. Capitalize on trigger-based tuning

Capitalize on trigger-based tuning

System optimization and ongoing monitoring are at the forefront of financial services organizations’ journeys to discover opportunities for efficiency and effectiveness. Customer segmentation, or the ability to group customers by similar attributes and transaction patterns, allows large financial services organizations to monitor their customer base more effectively. As these organizations continue to roll out customer segmentation methodologies to conduct more targeted monitoring, they require increased effort to complete tuning exercises. Larger financial services organizations are responding to this increased need by introducing a prioritized tuning approach.

Using automation software, financial services organizations can create scheduled jobs to extract data and provide predefined metrics such as scenario and rule effectiveness (the percentage of alerts escalated for further review divided by the total alert population) and the detection values of suspicious activity report (SAR) filings. These metrics provide insight into which rules are not as effective at identifying potentially suspicious activity, such as the presence of a high number of false positives or a significant population of SAR filings near the current thresholds.

As data availability increases and data quality improves, financial services organizations are moving away from tuning every 12 to 18 months in favor of analysis driven by defined trigger events. This method of tuning allows for more frequent and targeted tuning that addresses potential pain points as they arise, stricter adherence to methodology, and data-driven decision-making that is consistent and sustainable.

2. Gain valuable insights from targeted monitoring activities

Gain valuable insights from targeted monitoring activities

Targeted monitoring exercises yield valuable insights into potential improvements in Bank Secrecy Act (BSA) and AML practices. Exploring targeted monitoring approaches and implementing program-level changes can enhance the effectiveness of identifying suspicious activities. In this regard, large financial services organizations are focused on three areas for growth:

  • Customer segmentation
  • Categorizing SAR data
  • Strong relationships with regulators and law enforcement

Customer segmentation helps financial services organizations monitor and analyze their customers’ transactions through a finely tuned lens. Using data analysis, organizations can categorize their customers into distinct segments based on various attributes such as customer type, risk level, and transaction pattern. Segmentation enables organizations to tailor their monitoring more effectively by establishing unique monitoring thresholds for each segment. They can swiftly detect unusual or potentially suspicious activities specific to a particular customer group, thus reducing false positives and streamlining their BSA/AML efforts.

The review of SAR filing rationales can help organizations identify emerging risks by establishing connections between SAR filings and the specific typologies that triggered the alerts. This review can take many forms, ranging from requiring analysts to select a predefined drop-down category within the SAR form to using natural language processing1 (NLP) to identify common themes within the narrative field. By identifying commonalities among SAR filings, organizations can better align their customer risk rating methodologies, customer segmentation, and transaction monitoring rules to their specific risk profiles. Overall, SARs can provide detail and insight into AML monitoring, and organizations are becoming increasingly aware of these metrics.

Additionally, prioritizing an open and collaborative relationship with regulators and law enforcement can significantly enhance organizations’ abilities to stay informed about emerging red flags. The tendencies that regulators see across the field have helped many financial services organizations turn the lens on their own customers and transactional data to identify if they, too, are at risk for possible missed activity. Large financial services organizations are executing targeted custom monitoring queries based on feedback of emerging risks from regulators and law enforcement as a secondary measure of suspicious activity identification.

Regulators are not the only source of beneficial information. Many organizations also rely on different news outlets, peer groups, or industry professionals. This information gathering helps organizations remain up to date with emerging and evolving regulations by allowing for a more targeted approach to AML program changes. It also highlights the importance of determining which outlets organizations might deem as AML regulatory leaders.

3. Take care when exploring machine learning and artificial intelligence

Take care when exploring machine learning and artificial intelligence

Machine learning (ML) and artificial intelligence (AI) are disruptive technologies that many financial services organizations have on their radar. A few organizations are exploring these tools in terms of incorporating them into their AML transaction monitoring processes.

For example, financial services organizations can use machine learning to identify trends in data to build more targeted monitoring for diverse product types and increased customer and transaction volumes. As technology matures, use cases could become increasingly widespread throughout all AML programs.

Given the immaturity of the technology, some financial services organizations struggle with explaining new advanced techniques in a way that satisfies regulators. Such organizations experience more success when they can effectively communicate to regulators the entire design and implementation process.

To move forward with these technologies within an AML monitoring program, it is imperative to have extensive documentation explaining the rationale for the model. While AML compliance still requires human intervention, using ML and AI can help analysts focus time on effectively mitigating risk to organizations and the wider financial community.

4. Use cloud-based data warehouses to standardize data

Use cloud-based data warehouses to standardize data

Organizations across the financial services industry recognize the advantages cloud-based data warehouses can provide their BSA/AML programs as they search for a single source of truth regarding the data they work with daily.

Data warehouses aggregate data and store it in a centralized location, enabling data analysts to easily access more complete and accurate information across multiple systems. Agile analytical architecture on cloud-based technology allows analytics efforts to become more effective and useful across financial crime programs. Data warehouses can facilitate master data management exercises such as data quality, data validation, customer segmentation, and entity resolution.

Data warehouses also allow groups to pull data they previously might not have been able to aggregate and view it using additional functionality such as dashboards and reports. Overall, the standardization and centralization of data through a cloud-based data warehouse can lead to further insight and understanding on program data metrics that might previously have been overlooked.

5. Improve organizational efficiency with advanced analytics exercises

Improve organizational efficiency with advanced analytics exercises

Cutting-edge analytics techniques enable organizations to complete everyday tasks more efficiently and comprehensively than ever before. Organizations can explore advanced AML program components including:

Network analysis

Network analysis is a technique to better understand the relationships between entities at an organization. Large financial services organizations use network analysis to enhance:

  • Detection of frequently interacting accounts that might participate in the placement and layering of funds for money laundering
  • Identification of human trafficking rings when used in combination with known red flags such as immediate withdrawals of deposited funds
  • Identification of parties that frequently interact and have similar attributes, which might indicate transfers between one entity’s multiple accounts in support of entity resolution

Predictive risk analytics

The goal of predictive risk analytics is to use past events to predict future outcomes. Organizations can use previously decisioned alerts to triage future alerts for review, including the hibernation of potential false positives. This activity has received a lot of regulatory scrutiny, as event triage might result in suspicious activity not being reviewed in a timely manner.

Natural language processing

NLP derives insight from text sources. AML compliance is littered with text-based information such as investigation comments, SAR filing rationales, and negative news searches. NLP techniques like text classification and keyword extraction can identify trends in alert escalations and SAR filings that might require more targeted monitoring. Sentiment analysis2 can help analysts review mentions of customers in the media.

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These five tactics can be applied to many areas of AML programs, and organizations are just beginning to acknowledge the positive impacts the tactics might be able to create.

As technology and BSA/AML regulations continue to evolve, the transformative potential that advanced analytics holds for financial services organizations worldwide is becoming clearer.

Outdated AML strategies aren’t cutting it anymore, but we can help with guidance and insights into effective approaches to keep your business up to date. Crowe specialists can help organizations better understand their AML programs and more effectively address emerging risks while maintaining sustainability and efficiency. We combine a client-first approach with deep specialization in AML compliance to give you more.

1 Natural language processing refers to the area of artificial intelligence that enables computers to understand text and spoken words (like how humans would) in order to perform repeatable tasks.

2 Sentiment analysis is an approach within NLP that aims to identify the tone (positive, neutral, or negative) within a body of text.

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Tapan Shah
Principal, Financial Services Consulting
Corey Minard
Corey Minard
Financial Services Consulting
Alex Ziegler
Financial Services Consulting