Artificial Intelligence and AML.

The regulatory context and the need for Automation

In recent years, European regulations on anti-money laundering (AML) and counter-terrorism financing have become increasingly stringent. Regulation (EU) 2024/1624, part of the “AML Package,” introduces important provisions in the field of anti-money laundering, including enhanced management of the frequency of client verification checks. Based on a risk-based approach, it requires obligated entities—such as banks and other financial institutions—to conduct periodic and ongoing reviews of clients based on the associated risk. The frequency of these checks is tailored according to the risk assessment and the nature of the business relationship.

Continuous monitoring obligations have been strengthened to identify any significant changes in customers’ risk profiles. This includes the collection and updating of data on the beneficial owner and other necessary information to ensure ongoing due diligence.

These requirements have placed considerable pressure on operators, who must manage increasing volumes of checks while ensuring compliance with increasingly complex regulations. In this context, automation appears to be the only efficient way to address these complex and repetitive processes.

The use of advanced technologies, such as Artificial Intelligence (AI), in the field of AML and compliance could mark a significant breakthrough for companies operating in a progressively demanding regulatory environment.

While AI seems to be a strategic tool for optimizing AML procedures, it is crucial to understand its limitations and strengths. For these tools to realize their full potential, it is essential to recognize the distinct and complementary roles of databases and AI, as well as to adopt a strategy based on data quality and targeted automation.

The European Parliament defines AI as:

“[…]the ability of a machine to display human-like capabilities such as reasoning, learning, planning and creativity. AI enables technical systems to perceive their environment, deal with what they perceive, solve problems and act to achieve a specific goal. The computer receives data – already prepared or gathered through its own sensors such as a camera – processes it and responds. AI systems are capable of adapting their behaviour to a certain degree by analysing the effects of previous actions and working autonomously.”

When discussing AI in the context of compliance, it’s important to start with a fundamental premise highlighted in the European Parliament’s definition: AI works towards a specific goal by processing pre-existing data. AI does not generate new information; it simply processes, examines, analyzes, and correlates data that already exists within a defined framework, such as a catalog or database.

The complementary roles of databases and Artificial Intelligence

Databases containing customer verification information (or data on suppliers and partners) and risk exposure assessments form the foundation of information management. They store, organize, and secure data, ensuring it is accessible and persistent over time.

AI, on the other hand, stands out for its ability to analyze large volumes of data, identify patterns, and provide near-real-time insights. AI uses structured data from databases to address specific needs, such as transaction monitoring or detecting irregularities, adding value by identifying trends and flagging anomalies.

This relationship is essential. A well-structured database ensures that data is consistently available and up-to-date, a fundamental requirement for accurate AI analysis. Without high-quality data, AI systems may generate inaccurate results, waste resources, and increase the likelihood of errors.

The impact of data quality on efficiency

The effectiveness of any technology, including AI, depends on the quality of the data. The principle “Quality in, quality out” is key in AML processes. Incomplete or outdated data compromises the entire analysis cycle, leading to more false positives and slowing down the identification of genuine risks.

SGR is a leader in developing specialized databases for proper due diligence, designed to meet companies’ regulatory needs. Each database created by SGR adheres to rigorous standards of accuracy, timeliness, and organization, providing a solid foundation for integration with AI solutions.

Precise information, such as complete identifiers (e.g., name, date and place of birth, tax identification numbers) and regular updates, not only enhances the efficiency of onboarding or counterpart verification but also reduces the operational burden on analysts. This allows them to focus on higher-value tasks.

Automating compliance for greater efficiency

The adoption of AI enables the automation of repetitive and complex processes, optimizing time and resources. For example, in Automated Monitoring, AI examines thousands of daily operations, identifying anomalies and warning signals in real time. In systems like name monitoring and transaction monitoring, advanced matching processes allow continuous examination of sanction lists, PEPs (Politically Exposed Persons), and watchlists, with immediate notifications about significant changes. This proactive approach helps organizations manage changes in risk dynamics promptly, ensuring they are not overlooked due to resource constraints, while also preventing problems before they escalate into more severe situations. This significantly reduces reaction times as well.

These functions are already available in Daily Control: the solution developed by SGR allows for the centralization of data management and monitoring of global sources, as well as managing corporate data such as financial statements, company records, and structural changes. Using our platform, clients can already respond quickly to warning signs, such as suspicious changes in a company’s structure or modifications to its administrative bodies.

A hybrid approach: technology and Human Intelligence

AI cannot replace human judgment in complex decision-making but can assist analysts by improving operational quality and enabling them to focus on tasks that add greater value. The synergy between AI and human expertise, combined with high-quality databases like those provided by SGR, helps organizations meet regulatory challenges effectively, ensuring compliance and competitiveness in a fast-changing environment.

For example, experts must assess whether flagged transactions are truly suspicious or if there is a valid explanation, such as a legitimate commercial transaction. In this case, AI serves as a powerful tool for filtering and analyzing large data sets, while human experts evaluate the context, customer history, and any legal or reputational risks before making a final decision.

Final decisions—such as reporting suspicious transactions (STRs) or conducting a risk assessment—require solid expertise to interpret the data accurately and assess the legal and reputational implications. AI’s role is not to replace human input but to create a hybrid system where technology accelerates and refines analysis, enhancing speed and accuracy.

Enhancing Human Intelligence in compliance, AML, Financial Crime, and Fraud Prevention

To maximize the potential of human intelligence in these areas, it’s essential to integrate human expertise with advanced technological tools:

• Specialized Training: Continuously update experts on regulatory changes, emerging risks, and best practices through targeted training programs and participation in industry conferences.
• Informational and Technological Support: Equip teams with reliable databases and advanced analytical tools, such as those developed by SGR, to optimize analysis and enhanced due diligence.
• Focus on Critical Activities: Reduce reliance on resources for repetitive tasks, enabling human intelligence to focus on interpreting complex patterns and evaluating risk scenarios where intuition and experience are crucial.

Conclusion

To address regulatory challenges effectively, organizations must adopt an integrated approach based on:

1. High-quality, validated, and constantly updated data.
2. Automation to reduce operational burdens and improve the speed of responses.
3. Human oversight to ensure accurate interpretations and informed decisions.

This combination of advanced technology and human expertise allows organizations to transform regulatory complexities into opportunities to optimize risk management, improve operational efficiency, and maintain a competitive advantage.