Application of Probability Theory in Data Visualization: Financial Risk Prediction

Abstract

This review explores the intersection of probability theory and data visualization in the domain of financial risk prediction. It examines how probabilistic models—such as Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), Monte Carlo simulation, stochastic processes, and Bayesian inference—serve as the backbone of uncertainty modeling in finance by reviewing previous studies. Simultaneously, it highlights the role of visualization in transforming abstract probability distributions into interpretable insights through dashboards, heatmaps, clustering, and interactive visual frameworks. Drawing on over 20 open-access sources, the review synthesizes applications across corporate profitability, systemic risk, portfolio optimization, credit default, exchange rate forecasting, ESG sustainability, start-up financing, and decentralized finance (DeFi). It concludes by identifying limitations—including data quality issues, computational complexity, interpretability challenges, and ethical/regulatory concerns—and proposes future research directions in robust probabilistic modeling, scalable explainable AI, standardized visualization practices, and fairness-aware risk systems. Together, probability and visualization provide complementary tools that are indispensable for navigating financial uncertainty in the 21st century.