Themes
Explainability and Compliance of AI algorithms in Finance:
We explore methods to enhance the transparency and interpretability of AI algorithms used in financial systems. In particular, we focus on ensuring that these algorithms comply with regulatory standards and ethical guidelines, promoting trust and accountability in financial decision-making.
Fraud detection and money laundering using AI:
This theme focuses on leveraging AI techniques to identify patterns and anomalies indicative of fraud and money laundering activities in financial transactions. It aims to enhance the speed, accuracy, and adaptability of detection systems while ensuring compliance with evolving regulatory frameworks.
Risk management with AI, and Computational & Mathematical modelling:
This research theme investigates the use of AI, computational techniques, and mathematical modeling to improve risk assessment and decision-making in financial systems. It aims to develop advanced tools that can simulate complex scenarios, predict potential risks, and optimize mitigation strategies.
Applications to Sustainable Finance:
This research theme explores how AI and quantitative methods can be applied to promote sustainable finance, including ESG (Environmental, Social, and Governance) investment strategies. It aims to support the transition to a more sustainable economy by enhancing decision-making, risk evaluation, and impact measurement in financial markets.
AI in Ecosystems:
This research theme investigates how AI can enhance understanding of financial ecosystems, focusing on the dynamics between fintech innovation, market behavior, and regulatory frameworks. Their work develops AI-driven tools to model systemic risk, optimize financial networks, and support sustainable, data-informed financial decision-making.