Towards Robust and Interpretable Graph Learning for Online Financial Services
PI: Dr Chao HUANG
Abstract:
In this project, our goal is to address the challenges faced by online financial services by developing robust, interpretable, and efficient methodologies and application tools for customer behavior modeling. We aim to empower online financial service providers to better understand the customer behavior dynamics of financial transactions and make sound decisions from noisy and sparse financial data. Our approach will involve leveraging advanced graph analytics techniques to model complex interactions and dependencies between various financial transactions and customer behavior dependencies. We will perform comprehensive experiments to validate the effectiveness and efficiency of our proposed learning solutions in various online financial service scenarios, such as fraud detection, credit scoring, market trend prediction, and risk management. With the development of robust and interpretable graph learning methodologies, we aim to provide decision-makers of online financial services with actionable insights and support decision-making to make better-informed decisions and provide more accurate and efficient financial services to their customers. Our systems will be designed to be user-friendly and easy to implement, ensuring that they can be seamlessly integrated into any online financial services platform. Ultimately, our project aims to create a more transparent and reliable financial ecosystem that benefits both financial service providers and their customers.