Graph representation and knowledge graphs provide unique opportunities in representing complex systems that are challenging to model using tabular data.

They effectively represent complex systems with a large number of entities, multiple entity types, different relationship types, and patterns.

This provides unique opportunities in using graph and graph-based solutions in financial services, ranging from modeling the financial market's transactional systems to financial crime detection.

In addition to the benefits of graph representation, graph native machine-learning solutions such as graph neural networks, convolutional networks, and others have been implemented effectively in many financial systems.

Graph representations allow researchers to model inductive biases, encode domain expertise, combine explicit knowledge with latent semantics, and mine patterns at scale. This facilitates explainability, robustness, transparency, and adaptability—aspects that are all uniquely important to the financial services industry. Recent work on numeracy, tabular data modeling, multimodal reasoning, and differential analysis, increasingly rely on graph-based learning to improve performance and generalizability. Additionally, many financial datasets naturally lend themselves to graph representation—from supply chains and shipping routes to investment networks and business hierarchies.

In recent years, knowledge graphs have shown promise in furthering the capabilities of graph representations and learning techniques with unique opportunities such as reasoning.

Reasoning over knowledge graphs enables exciting possibilities in complementing the pattern detection capabilities of the traditional machine learning solutions with interpretability and reasoning potential. This path forward highlights the importance of graphs in the future of AI and machine learning systems.