Problem Statement & Proposed AI Framework for Predicting & Assessing Financial Risk

Due to the markets are getting more complicated, there is more data to deal with, and they have to follow the rules, banks all over the world are having a harder time accurately predicting and managing different types of risk exposures. Traditional econometric models are helpful, but they don't work well with datasets that have many dimensions or show how markets move in a non-linear way. This research introduces a novel hybrid AI framework that amalgamates machine learning algorithms with deep learning architectures. This proposal will change the way we look at financial risk completely. The proposed system uses advanced natural language processing techniques to merge structured financial indicators with unstructured text data sources. This lets you do a full risk assessment that older versions can't do. We combine different learning methods that include long short-term memory networks, convolutional neural networks, and transformer architectures with random forest, gradient boosting, and support vector machines. The framework has explainable AI processes that use SHAP and LIME methods to make AI-driven financial choices clearer. This approach ensures adherence to rules and fosters mutual trust among participants. Many tests have shown that this model is better at making predictions than older statistical models. It also helps you make choices, which is important for financial uses.