Enhanced Decision Support System for Financial Risk Assessment Using Hybrid Fuzzy Logic and Machine Learning

Abstract

This paper proposes an advanced framework for financial risk assessment leveraging a synergistic blend of fuzzy logic and machine learning techniques. In today’s volatile financial markets, accurate risk evaluation is imperative for effective decision-making. Traditional methods often fall short in handling uncertainty and complexity inherent in financial data. Hence, this study introduces a novel approach integrating fuzzy logic for handling imprecise information and machine learning algorithms for predictive modeling. The proposed system employs a hybrid model that amalgamates fuzzy inference systems with ensemble learning, harnessing the strengths of both paradigms. Fuzzy logic aids in capturing vague and qualitative aspects of financial risk assessment, while machine learning algorithms like random forests or gradient boosting enhance predictive accuracy by learning from historical patterns and trends. Furthermore, the study presents a comprehensive evaluation using real-world financial datasets to demonstrate the efficacy and superiority of the proposed hybrid approach compared to conventional methods. Performance metrics such as accuracy, precision, and recall are utilized to validate the system’s effectiveness in risk assessment across different market scenarios. This research contributes to the burgeoning field of financial risk management by providing a robust decision support system capable of handling uncertainties inherent in financial markets, thereby assisting stakeholders in making informed and proactive risk mitigation strategies

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