<p>Due to rapid technological advances, evolving legal regulations, and economic uncertainty, organizations today face increasing challenges in managing financial risks. It is common for traditional techniques to struggle to handle vast amounts of real-time data, respond to changing market conditions, and manage the inherent unpredictability of financial data. The purpose of this study is to overcome these obstacles by (i) creating a hybrid financial risk management framework that combines artificial intelligence (AI) with fuzzy logic, (ii) increasing the accuracy and robustness of financial risk predictions through the use of the Random Forest (RF) algorithm, and (iii) increasing the interpretability and transparency of decision-making through the use of fuzzy reasoning. By utilizing random forests, the proposed Adaptive Financial Risk Management System (AFRMS) constructs numerous decision trees and employs a voting mechanism to achieve robust risk classification. The incorporation of fuzzy logic enables the management of ambiguity and facilitates human-like reasoning, leading to risk assessments that are both accurate and interpretable. The system has been proven to deliver excellent performance in recognizing potential hazards and providing advanced decision support through extensive experimental validation on large financial datasets. As a result, risk management is facilitated by better-informed decision-making and stakeholder confidence is increased.</p>