The financial industry leverages machine learning in more ways than just finding the right alpha signal. It grapples with supply chains, business processes, marketing, churn, fraud, and money laundering, all while maintaining compliance with the various regulatory frameworks it is beholden to. Due to the sheer volume of wealth being handled by the financial industry and its critical role in everyday life, it has been a lucrative target for a wide spectrum of ever-evolving bad actors. With each successive iteration of this workshop, we have attempted to capture the breadth of these actors - fraudsters, money launderers, market manipulators, and potentially nation-state-level risks. The emerging advances in Generative AI make this a particularly exciting time to host this workshop. GenAI offers groundbreaking approaches to handling the various data types prevalent in the financial sector. From a security point of view, bad actors are actively using Generative AI creatively to thwart conventional defenses (e.g. voice cloning, better synthetic identities), and this workshop's audience would benefit from commonly applicable defenses & best practices against such threats. Last but not the least, there is now an increasing willingness from the financial industry towards deeper engagement and data sharing with academia.

This workshop aims to explore the intersection of Generative AI with the rich tapestry of financial data types, seeking to uncover new methodologies and techniques that can enhance predictive analytics, fraud detection, and customer insights across the sector. By harnessing these advancements in AI, we can pave the way to not only understand customer behavior but also anticipate their needs more effectively, leading to superior customer outcomes and more personalized services. Our objective is to shed light on the challenges and opportunities presented by the diverse data formats in finance. We aim to bridge the gap between the dominance of traditional models for tabular data analysis and the emerging potential of Generative AI to revolutionize the treatment of time series, click streams, and other unstructured data forms.

Most smart glasses have a small and limited field of view. The head-mounted display often spreads between the human central and peripheral vision. In this paper, we exploit this characteristic to display information in the peripheral vision of the user. We introduce a mobile peripheral vision model, which can be used on any smart glasses with a head-mounted display without any additional hardware requirement. This model taps into the blocked peripheral vision of a user and simplifies multi-tasking when using smart glasses. To display the potential applications of this model, we implement an application for indoor and outdoor navigation. We conduct an experiment on 20 people on both smartphone and smart glass to evaluate our model on indoor and outdoor conditions. Users report to have spent at least 50% less time looking at the screen by exploiting their peripheral vision with smart glass. 90% of the users Agree that using the model for navigation is more practical than standard navigation applications.