Financial Risk Assessment Model Based on Fuzzy Logic

In the rapidly evolving landscape of business operations, establishing a robust security domain prevention system in the Middle Office is of utmost importance. To achieve this, the integration of Choquet expectation-based methodologies offers a powerful approach. The Middle Office acts as a critical hub for various business processes, including risk management, compliance, and data protection. With the implementation of Choquet expectation theory, which encompasses the combination of multiple criteria and preferences, businesses can effectively assess and optimize their security domain prevention system. The establishment and optimization of a security domain prevention system based on Choquet’s expectations provide businesses with a comprehensive and tailored approach to protect their critical assets, maintain operational continuity, and safeguard sensitive data from emerging threats in the Middle Office environment. This paper constructed a Fuzzy Optimization Membership Estimation (FOME) for the computation of the feature vector. The proposed FOME model uses the Flemingo Optimization model for the evaluation of the feature vector in the business middle office. The FOME model effectively computes the Choquet expectation features for the analysis of the risk management of the feature vector in the middle office. Through the membership estimation with the FOME model, the model significantly computes the different attacks in the middle office. The analysis of the proposed FOME is evaluated for the conventional CICIDS dataset for the attack analysis. The simulation analysis stated that the proposed FOME model achieves a higher classification accuracy of 99.89% for attack detection.