Machine Learning in Financial Risk Management: Enhancing Decision-Making in Uncertain Markets
Authors:-Manoj Kumar
Abstract-The dynamic nature of financial markets, marked by volatility, uncertainty, and the influence of diverse global factors, necessitates robust and adaptive risk management strategies. Machine learning (ML), as a subset of artificial intelligence (AI), is increasingly being adopted in financial risk management to analyze large volumes of data, detect patterns, and make informed predictions. This paper explores the integration of ML techniques in financial risk assessment and management, emphasizing their role in improving decision-making, identifying potential threats, and optimizing portfolio strategies in uncertain environments. The study examines various machine learning models, such as supervised learning, unsupervised learning, and reinforcement learning, and their applications in credit scoring, fraud detection, market risk forecasting, and stress testing. Furthermore, the paper addresses challenges related to data quality, model interpretability, regulatory compliance, and ethical concerns, highlighting the need for transparent and responsible AI implementation. Through a comprehensive analysis, this paper underscores the transformative potential of machine learning in advancing financial resilience and decision-making efficiency in complex and fluctuating markets.
