Gerenciamento de riscos em processos de operações financeiras: análise comparativa de riscos e práticas mitigadoras em modelos normalizados

Abstract

The financial sector is increasingly exposed to risks such as fraud, operational failures, and cyber threats. Proactive risk management is essential for the continuity and integrity of operations, supported by standards such as ISO 9001:2015, ISO 31000:2018, and the Basel III Accord, and enhanced by the application of technologies such as Big Data and Artificial Intelligence. However, despite the diversity of standards and practices, there remains a gap in understanding their application and alignment with financial processes, especially regarding cybersecurity and digital fraud, as current standards do not thoroughly address emerging technological risks. This study aimed to analyze risks and mitigation practices in financial operations processes, based on standardized models and recent scientific literature, seeking to identify approaches, vulnerabilities, and gaps in the models of the standards. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 protocol, using the Web of Science and Scopus databases, focusing on publications from the last 10 years. Of the 437 articles initially identified, 67 were selected for the final sample. The analysis revealed fraud risk as the primary concern (47 occurrences), with internal fraud being the most prevalent (12 occurrences), followed by operational risk (35 mentions), credit risk (32 mentions), and cyber risk (28 occurrences). Weaknesses in internal controls (35 mentions) were identified as a critical factor in exacerbating risks. The most frequent mitigation strategies include strengthening internal controls, applying Machine Learning and Artificial Intelligence, proactive fraud monitoring (EWS/RFI), and GRC frameworks. The main thematic areas were "Fraud and Operational Risk" (19%), "Governance and Risk" (13%), and "Technology and Risk" (8%). A comparison with the standardized models highlighted significant gaps in addressing emerging technological risks. The study presents the need for updating existing standards and guidelines to explicitly integrate cybersecurity, digital governance, digital fraud management, and technological risks, in addition to encouraging and regulating the incorporation of AI, ML, and Big Data to enhance predictive analysis and strengthen the security of financial systems.