Exploring Interval-Valued Fermatean Neutrosophic Tactics for Empowering AI-Driven Financial Risk Frameworks: Compliance Automation, Fraud Detection, and Beyond

Abstract

The financial risk evaluation process, which includes the investigation of the risks related to loans, funding, and trading activities in economic decisions, is greatly utilized in advanced financial systems. However, nowadays in an inconsistent, fast, and digitalized world, conventional risk models are inadequate, particularly when there is ambiguity, inconsistency, or incomplete information provided in the data. In such scenarios, the interference of Artificial Intelligence (AI) is playing a crucial role. Not only are the large data sets dealt with by AI, but also the decision-making processes can be enhanced. Conventional mathematical tools cannot analyze the discipline whose limits are complex, ambiguous, and indeterminate. In this research article, to recognize and analyze these mathematical disciplines, Interval-Valued Fermatean Neutrosophic Numbers (I-VFNNs) are used, an argumentation of modern fuzzy logic. I-VFNNs are particularly mapped out for such circumstances where the information in the data contains uncertainty, ambiguity, or inconsistency. We have used Interval-Valued Fermatean Neutrosophic Numbers (I-VFNNs), an extension of modern fuzzy logic, to identify and analyze these mathematical constraints. In this article, firstly, a list of essential aspects is composed that are affected by Artificial Intelligence in financial risk models, like fraud detection and prevention, stress testing and scenario simulation, automation of regulatory compliance, behavioral risk analysis, enhanced predictive accuracy, dynamic risk modeling, and real-time risk monitoring, etc. All these components we visualized as mathematical disciplines, which are non-probabilistic, irregular, and multifaceted in nature. With the help of I-VFNNs, we portrayed these disciplines in the guise of numbers and demonstrated their influence, intensity, and indeterminacy in accordance with the impact of Artificial intelligence. The results demonstrate that I-VFNNs not only composed ambiguity superiorly, but also refined the disparity between various risk factors. In general, not only are modern ways to constructively assess financial risk models based on Artificial Intelligence (AI) developed, but new approaches in the inspection of indeterminate data using I-VFNNs are furnished in this study. By virtue of this model, in financial organizations, better decisions can be made, accelerate the recognition of risks, and put down the right consideration even in uncertain conditions. In the future, this model can also contribute to other innovative research areas such as international financial policies, large investments, and insurance institutions.