Advances in Machine Learning for Financial Risk Management: A Systematic Literature Review

Financial risk management is evolving rapidly with the integration of machine learning techniques, which offer sophisticated tools for managing complex and interconnected financial systems. Its crucial role in risk identification, assessment, mitigation, and prediction accounts for its significance. This systematic literature review analyzes the utilization of machine learning in financial risk management across 15 scholarly papers. The papers were selected from reputable journals available on databases such as ScienceDirect and IEEE. Only papers published during the last five years were selected to ensure their relevance. The selected papers include various machine learning algorithms, such as neural networks, deep learning, and ensemble methods, applied to financial risks such as credit, market, liquidity, and systemic risks. The research uses multiple methodologies, including but not limited to quantitative, empirical, and computational modeling approaches. The findings of the comparative analysis reveal that machine learning techniques are more effective and adaptive than traditional statistical methods at managing intricate risk patterns across a range of financial risk domains. Additionally, there is a clear trend toward leveraging big data and advanced analytics to enhance financial risk management practices. Academics and decision-makers from various industries can get valuable insights from the synthesis of existing literature, which will improve their comprehension of the progress in the financial risk management process utilizing machine learning techniques.