Financial institutions are increasingly facing complex challenges in risk management, as traditional methods struggle to predict and mitigate evolving threats in the financial markets. With the rapid pace of change and emerging risks such as economic downturns and cybersecurity issues, the need for advanced, data-driven tools has never been more critical. The novelty of this review presents a comprehensive analysis of AI-driven predictive analytics in financial risk management, offering a unique synthesis of recent advancements in credit risk assessment, fraud detection, and market prediction. Artificial intelligence (AI) and predictive analytics offer a promising solution by enhancing risk forecasting and optimizing decision-making processes. This review explores how AI-driven predictive analytics are transforming risk management into the financial sector, with a particular focus on improving credit risk management, fraud detection, and market predictions. By synthesizing the latest research, the review highlights the integration of machine learning, data mining, and real-time predictive modeling as key innovations reshaping traditional risk assessment methods. A qualitative analysis of recent studies and case reports reveals that AI techniques have significantly improved the accuracy of credit risk assessments and fraud detection, offering financial institutions real-time tools that enhance decision-making efficiency. The findings demonstrate that AI-powered predictive analytics provide more precise, data-driven insights, enabling financial institutions to proactively address potential risks before they escalate. These technologies contribute to better risk mitigation, investment optimization, and enhanced customer trust. However, challenges such as data privacy concerns, algorithm transparency, and the integration of AI into existing systems remain. Future research should focus on overcoming these barriers and further exploring AI’s potential across various financial domains, with an emphasis on improving transparency and tackling integration challenges to maximize its effectiveness in financial risk management.
DOI: https://doi.org/10.55463/issn.1674-2974.52.5.3
Publish Year: 2025
