With rapid technological advancements, organizations in the banking, financial services, and insurance (BFSI) sector are increasingly integrating artificial intelligence (AI) and machine learning (ML) into financial risk management. This systematic literature review examines these novel technologies’ challenges, applications, and ethical considerations. The analysis shows significant progress in using AI and ML for risk prediction, market analysis, and operational efficiency. However, it also points to significant implementation and standardization hurdles. Key issues include the complexity of data management, ethical and regulatory concerns, and the need for effective collaboration between humans and machines. The article underscores the need for continuous innovation and robust regulatory frameworks, as it ensures the adaptability and compliance of AI and ML in risk management (RM) in the financial sector. The report provides valuable insights into these technologies’ practical benefits and challenges by examining specific applications such as credit risk assessment, market RM, and integrating the Internet of Things (IoT) with big data. It offers concrete recommendations for practitioners and identifies areas for future research to improve the effectiveness and ethical use of AI and ML in the financial sector. This work should contribute to the theoretical expansion and practical implementation of advanced RM strategies and promote more stable and efficient financial markets.