Predictive Analytics in Financial Markets: A Cross-Disciplinary Approach to Risk Management

Abstract

Financial markets are increasingly relying on predictive analytics to manage risk and optimize investment strategies. This paper explores how data science, combined with insights from economics, behavioral finance, and computational modeling, can enhance decision-making in financial markets. We examine the use of machine learning algorithms for predicting market trends, detecting fraud, and assessing credit risk. Case studies from Australian financial institutions highlight the challenges of integrating predictive analytics into existing frameworks. The paper concludes with recommendations for fostering collaboration between data scientists, economists, and financial regulators to ensure ethical and effective use of predictive analytics.