Stock Predictions and Sustainability: Developed ML frameworks for predicting carbon emissions and understanding economic indicators like stock volatility.
Financial Applications: Explored AI for option pricing and financial compliance through data-checking strategies using large language models.
Sustainability and AI
The research in financial AI focuses on the development of machine learning frameworks designed to predict carbon emissions and analyze economic indicators such as stock volatility. By leveraging data-driven models, these frameworks aim to provide insights into sustainability and environmental impact while addressing financial market fluctuations. This work emphasizes the integration of financial and environmental data to create predictive models that are both economically and ecologically relevant.
Financial Applications
In the realm of financial applications, the research explores the use of AI for option pricing and ensuring financial compliance. By applying data-checking strategies using large language models, the work aims to enhance the accuracy and efficiency of financial decision-making processes. These innovations are geared toward improving risk assessment, regulatory adherence, and operational efficiency in financial markets.
