Money laundering hides the origins of illicit funds, undermining global financial systems. Its complexity makes detection, investigation, and reporting challenging for financial institutions.
AML measures, including customer monitoring, transaction tracking, and source verification, aim to prevent criminals from legitimizing illegal funds. These steps help combat money laundering, terrorism financing, and other crimes.
Since the financial crisis, AML fines have reached $56 billion, with $5 billion issued to US institutions in 2022. AML transaction monitoring and case management streamline investigations, ensure compliance, and enhance proactive threat responses.
What is AML Case Management?
AML case management affords a structured approach that financial institutions and other regulated industries can use to track, investigate and report suspicious activities. As an essential component of an organization’s AML compliance program, case management provides a clear method for managing the lifecycle of a case, from initial alert to escalation or resolution, enabling investigators to focus on the most critical issues and ensuring compliance with regulatory requirements.
Today, AML case management focuses on creating a central hub for operations, significantly shifting toward intuitive user interfaces. This development responds to the industry-wide need to escape outdated and siloed systems, akin to moving beyond a glorified Excel-based functionality to a more user-friendly, efficient platform. This shift is crucial for improving the efficiency of compliance teams and reducing the time analysts spend on reconciling cases, which is a significant cost in terms of human resources.
How Does an AML Case Management System Work?
An AML case management system centralizes customer information, linking all alerts, transactional activities and cases to individual customer profiles while managing, investigating and documenting alerts generated by monitoring systems. SEON integrates data from its AML platform with digital footprinting, device intelligence and fraud signals to provide a unified view of real-time risk decisioning.
Analysts can discover the first signals of suspicious activities in a customizable alert dashboard and conduct investigations by following procedures based on tailored investigation checklists, and document notes collaboratively with their team. Analysts can view items such as whether the person appeared on any AML screening lists, order and transaction information, and how risky their phone number, email and IP address are.
By setting alerts and status filters, compliance teams can determine and act on the appropriate workflow, assigning necessary actions on a transparent deadline system and ensuring that higher priority items receive immediate attention. Through automating tasks that used to take up much of an analyst’s time, case management systems can quickly highlight and prioritize the most severe alerts. This enables analysts to use their time effectively, closing alerts more swiftly and improving their risk management. Additionally, to tackle the problem of ‘false positive fatigue,’ contemporary AML systems like SEON’s use detailed context to make alerts more relevant and accurate, lessening the mental strain on analysts and allowing them to concentrate on critical thinking.
Analysts can follow case investigation checklists to ensure thorough investigations if an alert is escalated to a case. They can also analyze suspects, linked events and monetary movement while also uploading documents and adding notes to capture all pertinent details for a given case. If it’s determined a SAR needs to be filed, all the information needed can be found within case details, populated and filed directly with the regulatory authorities.
See how SEON’s AML system streamlines alerts, investigations, and workflows. Gain real-time risk insights, reduce false positives, and close cases faster with tools for seamless SAR filings and team collaboration.
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Defining an Efficient AML Case Management Solution
An efficient AML case management software combines real-time, direct-to-source data with advanced technological innovation, uniting AML and fraud detection and prevention within one platform – often under the FRAML moniker. With fraud and compliance data in one place, teams can connect the dots faster to easily unveil potential risks and patterns that might otherwise remain obscured.
Integrating artificial intelligence (AI), along with device intelligence, digital footprinting, and customizable machine learning algorithms, not only identifies irregularities but also provides transparent rationales behind the generated alerts to demystify decision-making processes, allowing analysts to understand and trust the AI’s recommendations, transitioning from cumbersome, manual processes to streamlined, intelligent systems like SEON’s. Efficient case management systems can pre-populate SARs with information from custom fields, leveraging generative AI to write SAR narratives and direct filing with regulatory authorities.
What is AML Compliance?
At the core of AML compliance is the process of Customer Due Diligence (CDD), which involves the collection and verification of vital information such as names, addresses, dates of birth, social security numbers or national government identifiers and other data that serves as the foundation for establishing and maintaining a customer’s profile with a financial institution.
A component of CDD beyond identification and verification, Know Your Customer (KYC) checks and assesses a client’s potential risks for illegal intentions. This proactive measure aids the understanding of the nature of a customer’s activities to ensure that they align with the legal and ethical standards set by regulatory bodies. When classified as high risk, Enhanced Due Diligence (EDD) is performed to provide a deeper analysis of the potential risks.
Digging Deeper: AML Compliance Screening and Monitoring
Part of AML compliance includes screening to help pinpoint individuals who may pose a higher risk due to their positions, associations or past behaviors. For specific individuals or entities, ongoing monitoring is imperative. This involves monitoring the size, frequency and nature of transactions with scrutiny to detect and investigate unusual or suspicious behavior and the tracking of customers against various lists such as Politically Exposed Persons (PEPs), their Relatives and Close Associates (RCAs), as well as sanctions, crime and financial watchlists, Special Interest Persons (SIPs) or Special Interest Entities (SIEs). Adverse media, a secondary layer of monitoring, flags potentially harmful information about individuals or entities by looking at media sources, including news articles, press releases and other public information, to uncover details that might indicate involvement in illicit activities, financial misconduct or other actions that could pose more significant risk. For example, adverse media can quickly highlight new developments, such as court judgments, allowing for prompt, informed decision-making based on the latest news.
Suppose a transaction is identified as suspicious without a clear lawful purpose. In that case, organizations are mandated to file a Suspicious Activity Report (SAR) with relevant authorities – FinCEN in the US, Fintrac in Canada, goAML in the EU and the National Crime Agency in the UK. Regulated entities, such as banks and financial institutions, securities and investment firms, insurance companies, gaming establishments, and money service businesses (MSBs), among others, must maintain detailed records of their AML checks, including the identities of clients, account files, business correspondences and findings from monitoring activities for a specified duration (typically five years) to support future investigations by regulatory bodies.
AML Case Management Workflow Challenges Solved
AML case management transforms how financial institutions approach potentially illicit activities, providing a streamlined, efficient workflow from the beginning to the end of an investigation. This enables analysts to enhance the speed and accuracy with which they respond to suspicious activities, facilitating a quicker close or escalation of cases. These systems foster enhanced productivity by managing lengthy investigations within a unified fraud and AML platform, allowing teams across organizational structures to create effective, collaborative workflows.
Centralizing data and automating the aggregation, information sharing and analysis processes, AML case management significantly reduces the time and resources previously devoted to manual tasks – speeding up investigative processes while minimizing human error. By breaking from traditional approaches, AML case management supports a holistic and adaptable strategy for financial institutions and modern fintechs to swiftly adapt to new threats and regulatory changes while maintaining the highest standards of integrity and accountability – safeguarding their interests, those of their clients and the broader economic ecosystem against the detrimental impacts of money laundering.
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Frequently Asked Questions
Measuring the effectiveness of AML case management involves assessing its ability to streamline processes, enhance risk detection and ensure regulatory compliance. Key indicators include the speed of case resolution, reduction in false positives and the accuracy of suspicious activity reporting. Additionally, measuring user satisfaction with the system’s usability and efficiency provides insights into its effectiveness in optimizing workflow and resource utilization.
An AML case management system is a comprehensive solution financial institutions and regulated industries use to track, investigate and report suspicious activities related to money laundering and other financial crimes. It provides a structured approach for managing the lifecycle of cases, from initial alert through escalation or resolution, enabling efficient allocation of resources and ensuring compliance with regulatory requirements. Modern systems
integrate real-time data, advanced analytics and artificial intelligence (AI) to streamline processes, enhance risk detection and improve operational efficiency.
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