EngD Alumni

The financial industry operates in a highly dynamic and interconnected ecosystem, posing complex challenges for predictive modeling and decision-making. Accurately forecasting financial performance, detecting fraud, assessing credit risk, and ensuring compliance require techniques that can capture temporal, relational, and contextual dependencies in financial data. This dissertation explores the application of Temporal Relational Graph Convolutional Networks (TRGCNs) combined with Financial Knowledge Graphs (FKGs) to address these challenges and advance analytics in finance.

The study introduces FintechKG, a financial knowledge graph constructed through a three-dimensional information extraction pipeline encompassing entities, temporal features, and domain-specific financial relationships. The TRGCN-based framework models both temporal and relational dependencies within FintechKG, enhanced with FinBERT embeddings and social media signals for richer feature representation. Using a financial performance prediction case study, a logistic regression model is applied to classify revenue trends, demonstrating the efficacy of combining relational and textual embeddings.

In addition, the dissertation presents a novel Temporal Credit Knowledge Graph (TCKG) framework to support corporate credit risk assessment. This framework leverages temporal embeddings, relational reasoning, and generative AI techniques to predict credit risk with high accuracy. A comparative analysis involving five models—including logistic regression, Relational Graph Convolutional Networks (RGCNs), generative AI Gemini models, and Graph Recurrent Neural Networks (GRNNs)—highlights the superiority of the Gemini-based approach in terms of performance, robustness, and explainability.

By integrating temporal learning, knowledge graphs, and generative AI, this research contributes a transformative methodology to financial analytics. It sets the stage for future innovations that merge structured reasoning with generative intelligence, enhancing decision-making in complex and dynamic financial environments.