Utilizing Artificial Intelligence to Improve UBO Detection and Compliance in Finance

Artificial intelligence (AI) is rapidly reshaping the financial industry, transforming risk management practices and enhancing compliance efforts, as outlined in Fintech Global News. The integration of technologies such as machine learning (ML), deep learning (DL), and generative AI (GenAI) is accelerating decision-making processes while improving the accuracy of traditionally human-dominated tasks.

As highlighted by Moody’s, AI is proving to be invaluable in the realm of Know Your Customer (KYC) procedures and enhanced due diligence. It aids in complex functions like intelligent screening and risk monitoring, thereby bolstering prevention and detection capabilities. By enabling financial institutions (FIs) to access vast, often fragmented data, AI promotes greater transparency and efficiency, ultimately improving compliance operations.

Olivier Morlet, a Money Laundering Reporting Officer (MLRO) and member of the Global Coalition to Fight Financial Crime (GCFFC), and Francis Marinier, Moody’s Industry Practice Lead, have discussed AI’s transformative impact in two crucial areas: regulatory compliance, especially in Ultimate Beneficial Owner (UBO) discovery, and social responsibility.

AI is revolutionizing data analysis and pattern recognition, both vital for identifying UBOs. The technology allows for rapid analysis of complex ownership data and can extract key information from unstructured texts through natural language processing (NLP). This enables AI to identify hidden ownership structures that would typically be difficult to uncover using manual methods. As global registers of beneficial ownership remain inconsistent, AI’s ability to reveal these concealed connections is essential.

Furthermore, AI-driven solutions can automate the process of identifying beneficial owners by cross-referencing various data sources. Through techniques such as entity resolution, AI can recognize when multiple records refer to the same entity, making data integration more straightforward and enhancing the accuracy of ownership mapping. This is especially valuable in light of the challenges posed by inconsistent global beneficial ownership registers.

Social Network Analysis (SNA) powered by AI is another powerful tool in uncovering UBOs. By mapping complex ownership structures and identifying key influencers within networks, SNA helps to reveal potential UBO links that might otherwise go unnoticed. This technology also supports investigations into financial flows, aligning with the «follow the money» principle to trace funds across intricate networks.

AI technologies like Optical Character Recognition (OCR) and NLP are also pivotal in managing unstructured data, such as PDFs. These technologies convert unstructured data into structured, searchable formats, facilitating the tracking of ownership chains and improving the quality of data required for effective AI/ML models.

Moreover, public-private partnerships (PPPs) play a crucial role in the development and deployment of AI/ML tools for financial crime prevention. The continued collaboration between regulators and financial institutions ensures that AI/ML technologies are used ethically and effectively, addressing the complex technical, legal, and practical challenges associated with their implementation.

A key concern in the application of AI/ML in compliance is mitigating biases. Transparent methodologies are essential to ensure that AI processes are open to examination, reducing risks related to model training and ensuring fairness and equity in the application of AI tools.

AI is significantly advancing the compliance landscape within the financial sector. By fostering innovation and leveraging data-driven tools, financial institutions can better navigate the complexities of modern financial systems, enhancing regulatory compliance and enabling more strategic decision-making.