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O Papel da Inteligência Artificial no Compliance de Prevenção à Lavagem de Dinheiro e seus Desafios Regulatórios
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A presente dissertação tem o objetivo de analisar como as instituições financeiras podem conciliar a utilização de sistemas de Inteligência Artificial (IA) na prevenção à lavagem de dinheiro (PLD) com as exigências regulatórias de transparência, supervisão humana e proteção de dados estabelecidas pelos
marcos da Lei de Inteligência Artificial (AI ACT) e do Regulamento Geral sobre a Proteção de Dados (RGPD), sem comprometer a eficiência operacional. Os resultados evidenciam que a IA oferece benefícios significativos, como maior precisão na detecção de atividades ilícitas e otimização de custos operacionais a longo prazo, especialmente devido à automação de processos e à redução de falsos positivos. No entanto, enfrenta desafios consideráveis, incluindo altos custos iniciais de implementação, viés algorítmico e complexidade regulatória. Por outro lado, a pesquisa identificou que, embora o cumprimento das exigências da Lei de Inteligência Artificial e do RGPD imponha adaptações operacionais significativas, ele pode atuar como um catalisador para a inovação e a modernização das práticas de compliance. Portanto, com estratégias robustas de governança de dados, explicabilidade e colaboração entre reguladores e instituições financeiras, é possível transformar os desafios regulatórios em oportunidades, fortalecendo a eficácia dos sistemas de PLD e promovendo maior transparência e confiança no setor financeiro.
The present dissertation aims to analyze how financial institutions can reconcile the use of Artificial Intelligence (AI) systems in Anti-Money Laundering (AML) efforts with the regulatory requirements for transparency, human oversight, and data protection established by the frameworks of the Artificial Intelligence Act (AI ACT) and the General Data Protection Regulation (GDPR), without compromising operational efficiency. The results highlight that AI offers significant benefits, such as greater accuracy in detecting illicit activities and longterm operational cost optimization, particularly due to process automation and the reduction of false positives. However, it faces considerable challenges, including high initial implementation costs, algorithmic bias, and regulatory complexity. On the other hand, the research identified that while complying with the requirements of the AI ACT and GDPR imposes significant operational adaptations, it can act as a catalyst for innovation and the modernization of compliance practices. Therefore, with robust strategies for data governance, explainability, and collaboration between regulators and financial institutions, it is possible to transform regulatory challenges into opportunities, strengthening the effectiveness of AML systems and fostering greater transparency and trust in the financial sector.
The present dissertation aims to analyze how financial institutions can reconcile the use of Artificial Intelligence (AI) systems in Anti-Money Laundering (AML) efforts with the regulatory requirements for transparency, human oversight, and data protection established by the frameworks of the Artificial Intelligence Act (AI ACT) and the General Data Protection Regulation (GDPR), without compromising operational efficiency. The results highlight that AI offers significant benefits, such as greater accuracy in detecting illicit activities and longterm operational cost optimization, particularly due to process automation and the reduction of false positives. However, it faces considerable challenges, including high initial implementation costs, algorithmic bias, and regulatory complexity. On the other hand, the research identified that while complying with the requirements of the AI ACT and GDPR imposes significant operational adaptations, it can act as a catalyst for innovation and the modernization of compliance practices. Therefore, with robust strategies for data governance, explainability, and collaboration between regulators and financial institutions, it is possible to transform regulatory challenges into opportunities, strengthening the effectiveness of AML systems and fostering greater transparency and trust in the financial sector.
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Inteligência Artificial Compliance Prevenção à Lavagem de Dinheiro (PLD) RGPD Artificial Intelligence Compliance Anti-Money Laundering (AML) GDPR