Machine Learning and Big Data Analytics in Financial Risk Assessment

Abstract

In the evolving landscape of financial risk management, machine learning (ML) and big data analytics have emerged as key drivers in transforming risk assessment processes. This paper explores the integration of these technologies to enhance predictive accuracy, optimize decision-making, and improve the efficiency of financial institutions in managing risk. By leveraging vast amounts of unstructured and structured data, coupled with advanced machine learning algorithms, financial institutions can better forecast risks, identify potential fraud, and improve credit scoring models. The application of big data analytics in financial risk assessment not only provides insights into traditional financial risks but also introduces innovative solutions for emerging risks, thus offering a robust framework for managing uncertainty in financial markets.