Big Data and Machine Learning Methods in Financial Risk Prediction

Abstract

Financial risk prediction has become increasingly critical as modern financial systems face rising uncertainty, complex market dynamics, and rapidly expanding data sources. Despite progress in traditional risk assessment, recent crises have revealed persistent limitations in capturing nonlinear and emergent risk patterns. This study aims to systematically examine how big data and machine learning methods are reshaping financial risk prediction and to identify the key trade-offs between predictive performance and model interpretability. We review major methodological developments across supervised and unsupervised learning, deep neural networks, graph-based models, multimodal architectures, and emerging large language model–driven frameworks. The findings show that advanced models, especially deep, graph-based, and multimodal approaches, consistently outperform traditional statistical techniques, yet often do so at the expense of transparency, giving rise to a growing focus on explainable and human-centered financial AI. We also highlight emerging trends such as multimodal financial foundation models, privacy-preserving federated learning, and inherently interpretable architectures. This review provides guidance for researchers and practitioners seeking to build financial risk models that are both accurate and trustworthy in real-world applications.