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Real-time Fraud Management

Project Title: Real-time Fraud Management based on Graph Analytics and Learning

Principal Investigator: Professor He Bingsheng

Summary: The project focuses on addressing critical challenges in real-time fraud detection within future digital finance applications. Graph analytics can be used to detect money laundering and various types of fraud by examining large volumes of transactions, offering clear insights to investigators. Due to the high velocity of transactions and the expansion of large-scale graphs in pervasive digital finance environments, there is a growing need for more sophisticated analytics tools. This project aims to enhance fraud detection capabilities by researching and developing advanced graph analytics, graph neural networks, and network embedding methods. These methods will leverage emerging parallel hardware, such as graphics processing units (GPUs), to enable real-time processing. The project also plans to explore emerging graph learning and embedding technologies to improve the effectiveness of fraud detection. The project also aims to explore the explainability of the approaches used for fraud detection.

Scoring the Reasoning and Prediction of Financial Experts

Project Title: Extracting and Scoring the Reasoning and Prediction of Financial Experts’ comments from Textual Information

Principal Investigator: Associate Professor Huang Ke-wei

Summary: Successful financial decisions require useful information, but investors often face challenges with too much or misleading data. The project's research objective is to create a system along with new algorithms capable of automatically summarizing the financial forecasts of experts, as well as the explanations and reasoning behind these forecasts from a vast amount of textual data. Once the information is cleaned and extracted, the aim is to develop a scoring system that quantifies the quality of both the forecasts and the causal reasoning behind the predictions made by financial experts. This research is valuable for stakeholders in the financial markets, including regulators like MAS, professional investors, and retail investors. It can help verify analysts' recommendations, monitor public firms' forecasts, and protect investors from misleading information.

Investment Decision-making with Large Language Models

Project Title: Enhancing Financial Investment Decision-making with Retrieval-augmented Large Language Models

Principal Investigator: Chair Professor Chua Tat Seng

Summary: Existing large language models are mostly trained for the general domain and tend to struggle to correctly understand the various types of financial data due to the unique terminology and domain knowledge in the financial area. The application of large language models in vertical domains like finance is still underexplored. The project proposes the development of a retrieval-augmented financial large language model (FLLM) to improve decision-making in finance. The project aims to adopt a novel approach to develop domain-specific large language models (LLMs) that can effectively integrate and interpret diverse data from multiple channels timely and precisely by combining LLM and information retrieval (IR) models. Additionally, the reliability and accuracy of the FLLM on solving complex financial tasks will be enhanced by teaching it to perform automatic problem decomposition and leverage reliable external tools to derive more trustable results.

Predicting Debt Crisis with AI and Real-Time Big Data

Project Title: Predicting Debt Crisis with Artificial Intelligence and Real-Time Big Data

Principal Investigator: Assistant Professor Zheng Huanhuan

Summary: The project represents a new approach to improve the prediction of sovereign debt crises by addressing the limitations of traditional early warning systems. The project aims to enhance early warning systems for predicting debt crises using AI and real-time big data and, in the process, empower policymakers to proactively prepare for and navigate macroeconomic crises. Leveraging artificial intelligence (AI) and real-time big data extracted through text analysis of global news, commentary, and trending searches, the project promises substantial advancements over existing methods. In particular, it will pursue the following objectives:

  1. Constructing real-time data feeds to nowcast and forecast economic activities.
  2. Incorporating international spillover effects to improve crisis prediction.
  3. Innovating new methodologies to identify early warning signals.