Financial institutions face increasing threats from fraud and money laundering today, both of which carry severe financial consequences and reputational damage. To address these challenges, many organizations adopt FRAML – an integrated approach that combines fraud detection and anti-money laundering (AML) efforts. While this approach has significant benefits, it must be implemented correctly to ensure effectiveness and maintain the distinct goals of fraud prevention and AML compliance. Missteps, such as combining alerts or conflating investigations, can undermine the integrity of both processes. In this article, we examine the benefits and potential pitfalls of a FRAML approach, and how comprehensive fraud platforms such as DataVisor’s advanced layered model offer a yet purpose-specific approach. Understanding Fraud Patterns and Trends in Anti-Fraud Efforts Fraud detection requires a dynamic approach to keep pace with evolving fraud patterns and emerging fraud trends. Fraudsters continuously adapt their methods, exploiting vulnerabilities in systems and leveraging new technologies to carry out fraudulent activities. As a result, effective anti-fraud strategies must remain flexible and forward-looking, incorporating advanced analytics, artificial intelligence and machine learning models, and real-time monitoring to identify and mitigate potential threats. The ability to detect fraud hinges on understanding the behaviors and tactics employed by fraudsters. For example, analyzing historical data to uncover recurring fraud patterns can help organizations build predictive models, enabling them to anticipate and block fraudulent transactions before they occur. Additionally, staying informed about the latest fraud trends, such as account takeovers or synthetic identity fraud, equips businesses with the knowledge needed to strengthen their defenses. Why FRAML is Essential Fraud and money laundering are often interconnected. For instance, activities such as account takeovers (ATO) or synthetic identity fraud are frequently used as gateways for laundering illicit funds. Fraudsters exploit these weaknesses to gain access to financial systems, making it crucial to recognize the interconnected nature of these threats. A well-executed FRAML approach allows organizations to address both issues simultaneously, mitigating risks that may otherwise go undetected when treated in isolation. Unified Approach, Separate Objectives Integrating fraud and AML efforts streamlines operations and reduces redundancies, since data from fraud detection systems, AML monitoring tools, and customer databases can be shared across teams to create a holistic view of potential risks. This integration ensures consistent strategies and efficient workflows without duplicating resources. Another area of consideration is compliance. Financial institutions operate under stringent regulations for both fraud prevention and AML compliance, and failure to meet these requirements can result in hefty fines and reputation damage. FRAML helps organizations adhere to these fraud and AML regulations by enabling a coordinated response to threats that covers all the bases, in terms of compliance and reporting obligations. With FRAML, institutions gain a broader perspective on criminal activities, as well. By correlating fraud indicators with potential money laundering activities, teams can make more informed data-driven decisions and detect sophisticated schemes that may be exploiting gaps in coverage, especially when data exists in silos. However, despite the benefits of a unified approach to FRAML, it’s vital to maintain distinct objectives for each function, because the methods for detecting fraud differ significantly from those for identifying money laundering. Why Fraud and AML Require Different Workflows Fraud detection and anti-money laundering (AML) workflows differ significantly due to their distinct goals and operational challenges. Fraud detection focuses on identifying and preventing unauthorized transactions or suspicious activity in real time. This urgency often necessitates highly responsive systems with strong detection capabilities, but it also results in a high volume of false positives, which can strain resources. On the other hand, AML processes are compliance-driven, requiring the generation of suspicious activity reports (SARs) to document potential money laundering activities. SARs demand rigorous analysis and detailed documentation, making AML workflows more methodical and less immediate compared to fraud detection. One of the key challenges in AML is overcoming data silos, which can impede the flow of information between departments and systems, reducing efficiency and creating blind spots in investigations. AML workflows also rely heavily on regulatory tools such as sanctions and watchlists to identify individuals or entities flagged for illicit activities. These tools require regular updates and accurate matching to avoid missing critical threats or generating unnecessary false positives. While fraud detection and AML may share some overlap in the tools and techniques used, their fundamental differences in objectives—preventing immediate financial losses versus ensuring regulatory compliance—necessitate workflows that are tailored to their unique demands. How Fraud Prevention Differs from AML The methods for detecting fraud focus on event-based analysis – identifying immediate, high-risk activities such as an unauthorized transaction, an account takeover, or abnormal login attempts. Detecting fraud requires real-time (or near-real-time) responses to mitigate threats before they escalate. Identifying money laundering, on the other hand, requires analyzing patterns. Teams must examine aggregated transactional data over time to uncover unusual behaviors such as: ADD Placement: To disguise the origin of funds and make them appear less suspicious to banks and regulatory authorities, placement is becoming increasingly difficult to detect due to emerging value transfer methods such as cryptocurrencies, stablecoins, and other blockchain-based payment systems. Launderers exploit the gaps in legacy systems with fixed schemas. Layering: Criminals attempt to obscure the origin of illicit funds by moving them through a complex series of financial transactions. Structuring (a.k.a. smurfing): When large amounts of illicit money are broken into smaller transactions to avoid triggering regulatory compliance reporting thresholds or drawing attention from authorities. AML detection focuses on retrospective investigations, which requires a detailed analysis of trends over time along with customer profiles and counterparties. As an industry, we also need to focus on the predicate crimes that are driving the need for money laundering. Human Trafficking, Child Exploitation, the Drug Trade all have various signals and patterns, prior to the laundering stage. These distinct approaches highlight the need for separate workflows tailored to their specific objectives. Implementing FRAML Correctly: What Works and What Doesn’t Let’s take a look at some of the mistakes teams typically make when taking an integrated approach to FRAML, versus what they should be doing. Data Integration, Not Alert Combination: One of the most common misconceptions about FRAML is the idea of combining fraud and AML alerts into a single metric or score – an approach that is fundamentally flawed. While fraud detection and AML monitoring use similar datasets, they analyze the data through different lenses. An effective FRAML system integrates these data sources within a shared platform but separates the workflows. Distinct Investigative Processes: The tools required for fraud and AML investigations are very different. Fraud investigations often involve real-time or near-real-time responses to stop unauthorized transactions or account breaches, while AML investigations require a detailed analysis of transaction histories and customer behaviors. Combining these roles under a single process is not only inefficient, it’s counterproductive. And, given that regulatory changes are common, it’s even more complex. Different teams, different mindsets: A fraud analyst might investigate a flagged login attempt, focusing on device data and transaction patterns to determine if the event is fraudulent. An AML investigator, on the other hand, might analyze a series of transactions spanning months to identify suspicious patterns indicative of money laundering. Attempting to combine these workflows dilutes the effectiveness of both, leading to missed threats and compliance failures. Given these potential pitfalls, it’s critical to leverage multi-layered detection with a platform that integrates advanced technologies like AI and machine learning to analyze large-scale data and detect fraud, money laundering and other risks – but keeps the workflows and alerts separate. The platform must accomplish all of this, without having a negative impact on the customer experience. How DataVisor’s Advanced Data Orchestration Empowers FRAML DataVisor provides a multi-layered model that integrates fraud detection and AML monitoring into a single system, while maintaining separate workflows. By enabling your teams to share insights and leverage common datasets, without conflating their goals. With Datavisor, fraud teams can identify immediate threats like ATOs, while AML teams analyze patterns for potential regulatory violations. Here’s how it works: Comprehensive Data Integration: DataVisor aggregates data from IT systems, customer databases, and monitoring tools into a centralized repository, providing full visibility across fraud and AML indicators. Advanced Analytics: AI and machine learning algorithms identify event-based triggers for fraud detection and aggregated patterns for AML monitoring. These analytics ensure that each team has what they need to do their work, while also supporting customer due diligence and know your customer (KYC) processes by analyzing risk factors, providing risk scoring, and flagging potential issues during onboarding and ongoing monitoring. Real-Time Monitoring and Alerts: DataVisor enables real-time transaction monitoring and fraud detection while supporting retrospective AML investigations. Fraud teams can intervene in a timely manner, without compromising the thoroughness of AML compliance efforts. Separate Workflows, Shared Insights: With DataVisor, fraud and AML teams can leverage automation and separate, customizable workflows tailored to their unique goals. Seamless data sharing enables teams to optimize operational efficiency and collaborate effectively. Enhanced Decision-Making: By maintaining the integrity of fraud and AML processes while linking related data points, DataVisor empowers organizations to make informed decisions that mitigate risks, improving case management, enhance compliance, and optimize operations. The Future of FRAML: Unified but Distinct As financial crimes continue to evolve and become more sophisticated, an integrated approach to fraud and AML is essential. However, success requires more than simply combining resources or metrics; fraud and AML teams must adopt systems that support effective collaboration without compromising the unique needs of fraud detection and AML compliance. DataVisor offers a powerful platform and unified approach that supports distinct workflows for fraud and AML while leveraging shared data and insights and the latest advancements in fraud detection, helping teams address overlapping risks effectively, maintain compliance, and strengthen their overall approach to risk management. Explore our solutions, or request a demo today! View posts by tags: Related Content: Digital Fraud Trends Fraud Awards 2024: The Most Shocking and Sophisticated Schemes of the Year CPO Corner How AI is Defeating Real-time Fraud Quick Takes 3 Key Ways Machine Learning Powers Fraud Prevention about Kevin McWey Kevin McWey is Chief Revenue Officer at DataVisor. He has an extensive track record across fraud mitigation, regulatory compliance, financial crimes technology, and product leadership. Prior to joining DataVisor, McWey held prominent sales roles for companies like FiServ, IBM, FIS and Socure. about Kevin McWey Kevin McWey is Chief Revenue Officer at DataVisor. He has an extensive track record across fraud mitigation, regulatory compliance, financial crimes technology, and product leadership. Prior to joining DataVisor, McWey held prominent sales roles for companies like FiServ, IBM, FIS and Socure. View posts by tags: Related Content: Digital Fraud Trends Fraud Awards 2024: The Most Shocking and Sophisticated Schemes of the Year CPO Corner How AI is Defeating Real-time Fraud Quick Takes 3 Key Ways Machine Learning Powers Fraud Prevention