Synergy of ChatGPT and Graph Analysis in Finance
What is Neo4j, and how can you use it as a domain expert with ChatGPT?
Importance of Graph Analysis in Finance
Relational databases have their limits. That’s where Graph Analysis jumps it.
Graph analysis is a powerful method for exploring and understanding the relationships and connections within structured data. Created out of nodes (Entities) and edges (Relationships), you can build a visually understandable analytical network that is easy to decode.
In finance, these networks could consist of customers (nodes) and transactions (edges), visualizing who transfers money to whom over time.
Neo4j is one of the most famous tools for graph analysis, serving as a highly regarded graph database management system. With its intuitive graph-centric model, Neo4j makes exploring relational data simpler, allowing users to uncover insights that may be unreachable in traditional database systems.
In the financial sector, the significance of graph analysis, supported by tools like Neo4j, cannot be exaggerated. It's an area where understanding relationships and networks is critical for various applications.
Fraud Detection: Analyzing the relationships between different entities and transactions, graph analysis helps to identify unusual patterns and potential fraudulent activities.
Risk Assessment and Management: Understanding the interconnection of various financial instruments and entities is essential for accurate risk assessment.
Compliance and Regulatory Adherence: Financial institutions operate in a heavily regulated environment. Graph analysis can aid in ensuring compliance by mapping out the relationships and flows of transactions, making it easier to audit and monitor adherence to regulatory requirements.
Customer Relationship Management: Understanding customer interactions and relationships is vital for providing better services and offerings. Graph analysis can provide a holistic view of customer relationships and behaviors.
Market Analysis and Investment Strategies: Analyzing market networks and understanding the relationships between different market entities can provide valuable insights for investment strategies.
Neo4j is a complex tool that can be difficult to handle. Its usage is mostly limited to IT professionals, which limits its potential value as domain experts who have specialized knowledge are unable to use it. However, with the introduction of ChatGPT, this situation may have changed. Domain experts can now interact with ChatGPT to translate natural language into cypher language, which can then be used with Neo4j to identify fraudulent patterns, customer relationships, and more.
The Impactful Role of ChatGPT
Using ChatGPT together with Neo4j can be a game changer for domain experts.
Neo4j is used for understanding relationships between customers, transactions, risks, and more - ChatGPT is used to interact with Neo4j for non-technical stuff.
ChatGPT, with its advanced natural language processing capabilities, can act as an intermediary layer between domain experts and Neo4j. Domain experts can interact with ChatGPT using natural language to describe the information they seek or the analysis they want to perform. ChatGPT can then translate these requests into Cypher queries, which can be executed in Neo4j to retrieve the desired results. This way, domain experts don’t need a deep understanding of Cypher query language, yet they can fully leverage the power of Neo4j’’s graph analysis capabilities.
→ Positive side-effect: over time, domain experts are getting educated using Neo4j on their own through practical learning enabled by ChatGPT.
In addition, ChatGPT can provide additional support by interpreting the findings gathered from Neo4j, by transforming the complex graph-based data and connections into a more understandable format or graphic representation. This enables domain experts to concentrate on obtaining actionable insights rather than struggling with query syntax or data comprehension.
Natural language processing allows for the creation of more advanced and nuanced analyses. With the help of ChatGPT, domain experts can create precise queries and iteratively refine them based on feedback or additional information. This iterative interaction can produce more refined, accurate, and insightful outcomes, particularly beneficial in complex or evolving analysis requirements.
This is one step toward the democratization of analytical processes by giving:
The right tools to
The right people at
The right moment to take
The right decisions
By harnessing the synergy between ChatGPT and Neo4j, domain experts in finance can significantly enhance their analytical capabilities. They can navigate the complexity of financial networks and relationships more efficiently, detect anomalies, assess risks, and uncover valuable insights that might otherwise remain hidden.
Real-world examples (Prompts and Results)
Example 1: Fraud Detection
Prompt to ChatGPT: Identify potential fraudulent transactions among accounts with unusually high transaction volumes in the last 30 days by using cypher query language together with Neo4j.
ChatGPT's Translation to Cypher Query:
MATCH (a:Account)-[t:TRANSACTION]->(b:Account)
WHERE t.date >= date() - 30 AND size((a)-[:TRANSACTION]->()) > 100
RETURN a, b, t
ORDER BY size((a)-[:TRANSACTION]->()) DESCExample 2: Risk Assessment
Prompt to ChatGPT: Find the exposure of Bank A to high-risk assets and list the connected entities using cypher query language together with Neo4j.
ChatGPT's Translation to Cypher Query:
MATCH (bank:Bank {name: 'Bank A'})-[:HAS_ASSET]->(asset:Asset {risk: 'High'})-[:CONNECTED_TO]->(entity:Entity)
RETURN asset, entityExample 3: Market Analysis
Prompt to ChatGPT: Identify the top 5 companies with the highest transaction volumes in the tech sector over the past year by using cypher query language together with Neo4j.
ChatGPT's Translation to Cypher Query:
MATCH (c:Company {sector: 'Tech'})-[:HAD_TRANSACTION]->(t:Transaction)
WHERE t.date >= date() - 365
WITH c, sum(t.volume) as TotalVolume
RETURN c.name, TotalVolume
ORDER BY TotalVolume DESC
LIMIT 5Conclusion
The integration of ChatGPT with Neo4j demonstrates the potential of combining natural language processing and graph analysis, particularly in financial analytics.
By using natural language to query Neo4j, domain experts can gain a more intuitive, efficient, and insightful understanding of financial networks. This synergy enhances analytical capabilities and brings the financial sector closer to a data-driven and insightful paradigm, promoting better decision-making and innovation.
Ultimately, the integration of ChatGPT with Neo4j empowers financial professionals to explore the intricacies of financial relationships and networks with greater ease and accuracy.
Besides using ChatGPT together with Neo4j, there are a few other examples where ChatGPT could be helpful to domain experts:
Writing SQL Queries
Writing VBA code for Excel
Writing Python code for domain experts with technical skills