AI-Powered Risk Analytics: A Deep Learning Approach to Financial Market Stability
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V2I3P106Keywords:
Deep learning, RNN, CNN, systemic risk, autoencoders, artificial intelligence, early warning systemAbstract
Risk and its management and stability in the financial markets have become key factors to be considered in the financial world. Standard risk calculations provide essential models but do not suffice with regard to high-dimensional, non-linear, or, most importantly, intertwined financial data. This paper, ‘Deep Learning–Based Frameworks for Systemic Risk Analytics and Future Trending,’ puts forward the possible use of deep learning, more so Recurrent Neural Networks, Convolutional Neural Networks, and Autoencoders to gauge and predict systemic risks. With the help of feature extraction, temporal pattern identification, and unsupervised learning, the framework has covered the loopholes of early warning systems and promoted the development of regulatory control measures. That is why, in training, historical data before 2021 is used to relate the insights with economic depressions, such as the 2008 financial crisis and the negative effect of the COVID-19 pandemic in the year 2020 on stock prices. Our model stresses that the deep learning architectures used in the paper perform better and more efficiently in identifying high-risk conditions than traditional statistical models, especially in forecasting. The conclusions present an opportunity for financial institutions and the regulating authorities to incorporate the application of Artificial Intelligence into their systems, which brings incredible added value in terms of overall preparedness for threats. The concepts of the article also contain methods, facts comparison, flowcharts, and statistics
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