How to Build a Financial Knowledge Graph Step by Step Guide

As we now have the understanding of what financial knowledge graphs are, where they can be utilized, and what their benefits are in the financial industry from the last blog Why Financial Knowledge Graphs Matter in Modern Finance, we will now go one step deeper into the financial knowledge graphs to understand how we can build one for our use case and how we can benefit from it.

Building Financial Knowledge Graphs

Setting up and building a financial knowledge graph requires careful planning and a deeper understanding of the domain or the problem you are solving. We can take this as an step by step approach to break down the process and see how we can start building one. Below are the key steps and considerations when building a financial knowledge graph:

1. Define the Scope and Use Cases: Start by clearly defining the problem and objectives. Identify what the graph should achieve — for example, fraud detection, investment analysis, or regulatory compliance. This helps determine which entities and relationships (customers, accounts, transactions, devices, etc.) are essential for your use case.

2. Design the Schema: A strong schema (or ontology) forms the foundation of the graph. Define key entities and relationships that mirror real-world financial structures. You can also adopt existing standards like FIBO (Financial Industry Business Ontology) for consistency. The schema should include both static relationships (e.g., Company A owns Company B) and conceptual rules (e.g., Assets – Liabilities = Equity).

3. Identify and Gather Data Sources: Collect and integrate data from multiple sources, such as internal databases (transactions, loans, customers), external data providers (market feeds, credit bureaus), and unstructured sources (reports, news). Convert and align this information into a graph-ready format that matches your schema.

4. Data Integration, Mapping, and Entity Resolution: Unify data by mapping fields to the schema and resolving duplicates. For instance, merge records that refer to the same company under different identifiers. Use entity resolution techniques like fuzzy matching and master identifiers (e.g., Global Legal Entity ID) to ensure each real-world entity is represented once. High data quality and clean links are critical for graph accuracy.

5. Choose the Right Graph Database: Select a suitable storage technology. Property Graphs (e.g., Neo4j, TigerGraph) for performance and intuitive querying. RDF Triple Stores (e.g., GraphDB, Stardog) for ontology-driven reasoning and inference. Once selected, load nodes and edges according to your schema and apply rules to maintain integrity (e.g., preventing invalid transactions or missing relationships).

6. Test and Refine: Validate the graph with real queries and scenarios to ensure it delivers relevant insights. This stage often reveals missing data or modeling gaps. Continuously optimize query performance and refine the schema as your understanding of the domain grows.

7. Maintain and Govern: Treat your graph as a living asset. Set up automated pipelines to ingest new data, apply quality checks, and manage updates. Implement governance for security, access control, and versioning. As financial systems evolve (e.g., adding cryptocurrency or ESG data), update the ontology to reflect new domains.

8. Enable Usage (APIs, AI, and Visualization): To maximize value, make the graph accessible through APIs, dashboards, and AI tools. Visual explorers help analysts navigate relationships, while AI models can use graph data for predictions or reasoning (e.g., graph embeddings). Combining human expertise with AI ensures continuous improvement and reliability.

By following these steps, we can build a robust financial knowledge graph that is aligned with business needs. The core focus should remain on the quality of the graph’s knowledge, having a well thought out ontology and reliable linked data. With those in place, a financial knowledge graph can continually grow and support an expanding range of intelligent applications.

Examples of Financial Knowledge Graphs

To better understand how Financial Knowledge Graphs work in practice, here are a few notable examples of their real-world applications:

1. Simplified Financial Network Graph Imagine a graph connecting entities such as companies, industries, assets, jurisdictions, and people. A bank can use this type of graph to map relationships, for example, which companies operate in a given region, issue certain assets, or share key executives. This unified network helps analysts identify exposures, dependencies, and opportunities across diverse financial datasets.

2. Regulatory and Compliance Graphs (FIBO) Regulators and financial institutions use the Financial Industry Business Ontology (FIBO) to standardize data for reporting and compliance. By representing derivative contracts, swap positions, and financial instruments in a shared graph structure, organizations can aggregate and compare information seamlessly, reducing reporting costs and ensuring transparency across firms.

3. Supply Chain and Risk Graphs Banks use knowledge graphs to understand inter-company dependencies and supply chain risks. By combining internal client data with supplier networks and external events (like bankruptcies or natural disasters), these graphs can answer complex “what-if” questions, such as how a supplier’s financial trouble might impact loan applicants or credit exposure.

4. Fraud and Transaction Graphs Financial institutions deploy graphs to detect fraud by linking customers, accounts, transactions, and devices. These networks can expose hidden connections, for instance, multiple fraudulent accounts tied to the same address or device. Machine learning models can then use graph-based features (like connection patterns) to enhance fraud and AML detection with greater accuracy.

5. Investment and Market Intelligence Graphs FinTech firms and research analysts build graphs that merge public financial data, market news, and social sentiment. For example, a graph connecting S&P 100 companies with risk factors and executive movements helps analysts uncover insights like which companies share exposure to a certain risk or market event. These graphs feed AI models and recommendation systems, providing richer, more connected financial intelligence.

Financial knowledge graphs are being applied broadly, from internal banking operations to industry wide data sharing and open financial data analysis. These examples illustrate the versatility of knowledge graphs; whether it’s enhancing fraud detection, providing a comprehensive view of risk, streamlining compliance, or powering investment decisions. The common factor is connected data. By building and leveraging financial knowledge graphs, organizations gain the ability to "connect the dots" across all their information and which leads to deeper insights and more informed strategies.