Neural Networks in Inclusive Financial Systems: Generative AI for Bridging the Gap Between Technology and Socioeconomic Equity

Abstract

Advancing technology can improve the quality of and access to financial services for all strata of society, including the underserved. As such, countries are focusing on building inclusive financial systems – a complex network of actors, institutions, rules, and technologies that can equitably and effectively meet the financial needs of all members of society, particularly underserved populations. However, current sophisticated frontier technological applications are inaccessible to many emerging economies (EEs). At the same time, these nations are home to a large share of the global population, economic growth, and currently unbanked individuals.Access to affordable financial services is a crucial factor in improving the quality of life for communities across the world. With the continued rise of advanced technologies, emerging economies (EEs) are attempting to build inclusive financial systems capable of extending financial services to unbanked populations. On the other hand, large language models have emerged as powerful tools that enhance productivity in various aspects of work and life. Generative AI systems (GenAI) have the potential to bridge the gap between advanced technology and socioeconomic equality by allowing financial service providers in EEs to more easily deploy sophisticated and relatively expensive neural network applications. As an initial effort to explore this research space, the focus is on developing a common understanding of the problem and possible roles for generative AI in inclusive financial systems.