<![CDATA[The increasing complexity of operational risks in financial institutions has challenged the effectiveness of traditional risk management approaches. This study conducts a systematic literature review to examine how big data analytics and advanced analytical techniques enhance operational risk management (ORM) practices. Following a structured Systematic Literature Review (SLR) methodology based on the PRISMA framework, peer-reviewed articles indexed in Scopus were identified, screened, and synthesized to ensure methodological rigor and transparency. The review analyzes how descriptive, diagnostic, predictive, and prescriptive analytics are applied across the ORM cycle, including risk identification, measurement, monitoring, and mitigation. The findings indicate that big data analytics, supported by artificial intelligence and machine learning, significantly improve early risk detection, predictive accuracy, and real-time monitoring capabilities. Moreover, these technologies strengthen operational resilience and data-driven decision-making in financial institutions. This study contributes to the literature by providing an integrated overview of analytical approaches in ORM and identifying key research gaps, while offering practical insights for financial institutions seeking to adapt their risk management frameworks to an increasingly data-intensive environment.]]>
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