Generative AI in Financial Intelligence: Unraveling its Potential in Risk Assessment and Compliance

Abstract

This work intends to address the existing large gap in content and methodological structure in the interfaces of generative AI regarding specific and critical problems in the financial intelligence sector in detecting, investigating, and preventing the trajectories of financial crimes that can alter the behavior of financial services markets and, consequently, the targeted financial institutions. The refinement in the discriminatory capacity of generative models can provide an ideal response in risk management and financial compliance, especially in the context of the vast and heterogeneous information within the scope of large organizations. To this end, the objective of generating viable avenues is proposed for the future development of generative AI in both models and applications aimed at the dynamics of the potential hazards in the field of financial activity. The lines of work focus on the generation of text and a coherent semantic context for the application of both types of generative AI. Defense against money laundering in the environment of criminal and financial surveillance, concepts of risk linked to transactions, products, and other financial services, where the models are gradually developed and widely disseminated in a perspective of innovative tools, is planned as a voucher of transparency and cooperation. We conclude by proposing topics for future research to encourage the development of generative AI and help prospective researchers with novel integration into applications in the field of financial practice. The central suggestion is that financial institutions seem to be very far from the use of these instruments since they are conceptually distant from the current object of use and are difficult to implement. Results on financial and compliance costs seem to be the only line of work in this area to be compatible. The right incentives are needed to weigh more.