AI for LawFinTech
DESCRIPTION
Vision
The aim of the Archimedes LawFinTech Hub is to bridge the existing gaps between the fields of finance and law by exploring their intersections while leveraging the capabilities of AI methods and Large Language Models (LLMs) to tackle complex issues that span both areas. The Hub’s aims and vision will be informed by AI researchers, practitioners, and industry experts from different disciplines, creating a platform for collaboration while driving the development of comprehensive frameworks that effectively address the multifaceted challenges present in both finance and law. This interdisciplinary approach will facilitate a deeper understanding of how AI can be utilised to innovate and improve practices within these interconnected fields. Our vision is to also support AI democratisation using open sourced large language models and to cover the significant gap in the global financial and legal industry by developing multi-lingual domain models, enhancing accessibility and inclusivity, enabling accurate and comprehensive analyses across different languages and regions, and promoting transparency and openness to empower a broader range of users and stakeholders, thereby fostering innovation and equitable growth in both sectors.
Opportunities
The advent of Large Language Models (LLMs) has significantly advanced various domains through the power of natural language processing (NLP) and machine learning. In the realms of finance and law, LLMs hold the potential to revolutionise traditional methodologies by enhancing efficiency and the accuracy of predictive models. Maximising the benefits of LLMs necessitates fostering interdisciplinary research between the financial and legal sectors, which is essential for addressing the complex challenges inherent in these fields. For instance, in financial regulation and auditing, LLMs can significantly enhance compliance monitoring, fraud detection, and risk management by efficiently analysing extensive legal and financial documentation. Their advanced contextual understanding allows them to grasp complex terminologies, improving sentiment analysis and predictive model accuracy. Flexibility in transfer learning enables adaptation to new tasks with minimal data, while scalability supports real-time analysis, providing immediate insights from news articles, market information, reports, and social media. Additionally, LLMs' multimodal capabilities integrate text, numerical, and visual data, enriching analyses. Their interpretability enhances transparency and trust, and customization allows them to be tailored to specific financial instruments, market conditions, and legal contexts, thereby improving the robustness of regulatory and auditing processes. The convergence of finance and law through the application of LLMs can lead to innovative solutions that comprehensively address both technical and regulatory requirements. This interdisciplinary approach ensures that developed solutions are not only technologically advanced but also compliant with relevant legal standards, thereby improving the overall system's robustness and effectiveness. Furthermore, such collaboration can drive the creation of new frameworks and methodologies that better integrate financial and legal perspectives, ultimately leading to a more cohesive and efficient system for managing the intricate challenges of these interconnected fields.
For more information please click here: https://sites.google.com/view/lawfintechhub/home
RESEARCHERS
Grigorios Tsoumakas
Aristotle University of Thessaloniki
Ion (Ioannis) Androutsopoulos
Athens University of Economics and Business
Manolis Koubarakis
National and Kapodistrian University
Nikos Mamoulis
University of Ioannina
Pantelis John (PJ) Beaghton
Imperial College London
