Statistical Dangerousness is a novel concept that introduces a dynamic and probabilistic approach to risk assessments in complex systems. Unlike traditional models that focus on static data or the average outcomes, Statistical Dangerousness incorporates the statistical variability in a system and the probability of exceeding a critical threshold, providing a more comprehensive understanding of potential dangers. This method is particularly applicable to fields like finance, where markets are inherently volatile, and extreme events are often difficult to predict. In finance, Statistical Dangerousness enhances risk assessments by capturing fluctuations in market conditions, asset prices, and financial indicators, allowing for the identification of periods when the likelihood of surpassing dangerous thresholds is high. By integrating both variability and probabilistic analysis, this tool enables the forecasting of potential financial crises, such as market crashes or institutional failures, which the traditional models often overlook. It allows financial institutions and investors to understand the likelihood of extreme outcomes better, improving their decision-making and the development of risk management strategies. Moreover, Statistical Dangerousness can be used to optimize the stability of financial systems by proactively detecting rising risk levels, thus preventing financial catastrophes. By focusing on the possibility of extreme deviations, it provides a forward-thinking approach to finance, enabling more accurate predictions and the timely mitigation of risks. As such, Statistical Dangerousness represents a significant advancement in financial risk management, offering valuable insights for anticipating and managing the uncertainties that shape the financial landscape.