FAR-AI: A Modular Platform for Investment Recommendation in the Financial Domain

Javier Sanz-Cruzado, Edward Richards, Richard McCreadie

Abstract: Financial asset recommendation (FAR) is an emerging sub-domain of the wider recommendation field that is concerned with recommending suitable financial assets to customers, with the expectation that those customers will invest capital into a subset of those assets. FAR is a particularly interesting sub-domain to explore, as unlike traditional movie or product recommendation, FAR solutions need to analyse and learn from a combination of time-series pricing data, company fundamentals, social signals and world events, relating the patterns observed to multi-faceted customer representations comprising profiling information, expectations and past investments. In this demo we will present a modular FAR platform; referred to as FAR-AI, with the goal of raising awareness and building a community around this emerging domain, as well as illustrate the challenges, design considerations and new research directions that FAR offers. The demo will comprise two components: 1) we will present the architecture of FAR-AI to attendees, to enable them to understand the how’s and the why’s of developing a FAR system; and 2) a live demonstration of FAR-AI as a customer-facing product, highlighting the differences in functionality between FAR solutions and traditional recommendation scenarios. The demo is supplemented by online-tutorial materials, to enable attendees new to this space to get practical experience with training FAR models. VIDEO URL.