The goal of all-inclusive finance is to bring reliable and high quality financial service to everyone everywhere, rich or poor. Managing large scale multi-agent interactions lies in the very nature of the all-inclusive finance, and many emerging problems in this new space involve sophisticated cooperation and competition between a diverse range of entities, such as people, small businesses, online platforms, financial units and even fraudsters. For instance,
• Good customer service requires cooperative problem solving by customer and automated systems;
• Fraudulent transaction detection is a game between attackers and platform defenders;
• Mutual insurance needs to incentivize millions of people in order to be effective and low cost;
• A healthy online lending product requires good policies for cash flow management;
• A good investment recommendation needs to understand the intricate relations between companies, financial assets and economic environment.
Machine learning techniques, such as multi-agent reinforcement learning, algorithmic game theory, generative adversarial learning, imitation learning, graph neural networks, construction and reasoning over knowledge graph, building interpretable and fair models, are playing increasingly important roles in addressing these problems in all-inclusive finance. In this workshop, we will invite technical leaders from both all-inclusive finance platforms to talk about these emerging problems and their solutions, as well as experts from such as Matei Zaharia, Raluca Ada Popa, Virginia Smith, and John Duchi. We will also invite Fintech tech leaders to talk about current status and their views for all-inclusive finance.