Unlocking Insights: GraphRAG & Standard RAG in Financial Services

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GraphRAG​

GraphRAG (Graph Retrieval-Augmented Generation) is an advanced approach in natural language processing that enhances traditional Retrieval-Augmented Generation (Standard RAG) systems by incorporating knowledge graphs generated by large language models (LLMs).

This method involves constructing a structured knowledge graph from a set of documents, identifying key entities such as people, places, and concepts, and representing them as nodes in a graph structure.



These nodes are then clustered into semantic communities, allowing for the generation of more comprehensive and diverse answers to complex, multi-hop questions. By leveraging these structured knowledge graphs, GraphRAG significantly improves the quality and relevance of the generated responses.

Standard RAG and GraphRAG difference​

Standard RAG and GraphRAG differ primarily in their sources and methods of information retrieval.



Standard RAG relies on vector stores to retrieve relevant documents based on a user’s query. It ranks and selects the top documents, combines them with the query, and generates a final response using a language model. In contrast, GraphRAG utilizes knowledge graphs, which include entities, relationships, and document graphs. It extracts candidate entities, relationships, and concepts from the knowledge graph, ranks and filters these candidates, and then combines them with the query to generate a response.



This approach allows GraphRAG to leverage more structured and interconnected data, providing richer and more contextually accurate responses compared to the document-centric approach of Standard RAG.



Standard RAG and Graph RAG difference

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Standard RAG and GraphRAG Example​

Here is a simple example to demonstrate the difference between Standard and GraphRAG. Let's say you would like to know the latest risks in the insurance sector. You come across a McKinsey article titled "Navigating shifting risks in the insurance industry" and decide to analyze the current challenges it presents (Navigating shifting risks in the insurance industry | McKinsey). You're interested in how Standard RAG and GraphRAG might help you extract information from this article, especially when answering a question like "What are the common themes for the insurance industry?" Here's what you'd find using Standard RAG and GraphRAG.



Standard RAG focuses on retrieving and summarizing relevant documents, resulting in a more straightforward and concise response.

In contrast, Graph RAG does a whole data set reasoning and provides a more detailed and interconnected response by leveraging relationships between entities in a knowledge graph. This allows it to offer a richer context, highlighting complex themes like interconnected risk factors and the integration of AI in the insurance industry.



Standard RAG Output



GraphRAG output

How to determine a GraphRAG usecase​

To determine a GraphRAG (Retrieval-Augmented Generation) use case, start by identifying areas where complex relationships and contextual understanding are crucial. GraphRAG excels in scenarios where data points are interconnected, such as in knowledge management, recommendation systems, and fraud detection. Begin by mapping out the entities and their relationships within your domain. For instance, in insurance, entities could include policyholder, agents, policies and claims, with relationships representing policy and claim transactions.



Next, evaluate the potential benefits of using a graph structure over traditional methods. GraphRAG can enhance data retrieval by leveraging these relationships, providing more accurate and contextually relevant information. This approach is particularly useful in domains requiring deep insights and nuanced understanding, such as claims fraud detection, where customer profiles and claim transactions are interlinked. By constructing a knowledge graph, you can enable more sophisticated queries and generate richer, more informative responses.

Use cases that can benefit from the combined capabilities of Standard RAG and GraphRAG​

In the financial services sector, the integration of Standard RAG and GraphRAG can significantly enhance the depth and accuracy of insights. For instance, in the banking sector, the combined power of standard and Graph RAG can be instrumental in enhancing customer relationship management (CRM) and risk assessment. Standard RAG can pull in vast amounts of customer data, transaction histories, and market trends, providing a comprehensive view of a customer’s financial behavior. Meanwhile, Graph RAG can analyze the intricate web of relationships between customers, accounts, and transactions, identifying potential risks and opportunities. For example, it can detect unusual transaction patterns that may indicate fraudulent activity or highlight cross-selling opportunities by understanding the interconnected needs of customers. This holistic approach enables banks to offer more personalized services, improve risk management, and ultimately drive better business outcomes.



In the insurance industry, claims processing can be revolutionized by leveraging these combined capabilities. Standard RAG can efficiently retrieve relevant policy documents, historical claims data, and regulatory guidelines, while Graph RAG can map out relationships between various entities involved in a claim, such as policyholders, medical providers, and repair shops. This dual approach not only accelerates the claims adjudication process but also helps in identifying fraudulent claims by uncovering hidden connections and patterns that might be missed by traditional methods.

Developing an end-to-end copilot application using Combined RAG approach​

Here's a step-by-step guide on how to build an end-to-end copilot type application using combined RAG, which includes both the Standard RAG and Graph RAG.



End to End application development using Standard and Graph RAG

1. Define Use Case and Data​

• Use Case: Post-disaster claims management.
• Data: Historical claims, customer profiles, policy details, disaster impact data, geographical data, social networks, weather patterns.

2. Create and Populate Knowledge Graph​

• Data Collection: Gather data from internal and external sources.
• Data Modeling: Define schema for entities and relationships.
• Data Ingestion: Load data into the knowledge graph.

3. Index and Embed Data​

• Document Indexing: Index relevant documents.
• Embedding Creation: Generate embeddings for entities and relationships.

4. Set Up Retrieval Systems​

• Document Retrieval: Implement system to retrieve documents from vector store.
• Graph Retrieval: Implement graph queries to extract relevant entities and relationships.

5. Develop Ranking and Filtering Algorithms​

• Document Ranking: Rank and select top documents.
• Graph Ranking: Rank and filter graph data.

6. Integrate with Language Model​

• Combine Data: Merge retrieved information from both sources.
• Response Generation: Use a language model to generate the final response.

7. Develop User Interface​

• Frontend: Create user-friendly interface.
• Backend: Ensure seamless communication between components.

8. Testing and Validation​

• Test Scenarios: Validate accuracy and relevance.
• User Feedback: Refine system based on feedback.

9. Deployment and Monitoring​

• Deployment: Deploy in production.
• Monitoring: Continuously monitor and improve.

Example Workflow​

1. User Query: “Manage claims after a recent hurricane.”
2. Document Retrieval: Retrieve historical claims, policy details, and disaster impact reports.
3. Graph Retrieval: Extract geographical data, social networks, and real-time weather data.
4. Ranking and Filtering: Prioritize relevant information.
5. Response Generation: Combine data and generate a comprehensive claims management plan.
6. Output: Provide a detailed report with:
   • Historical claims and policy details.
   • Geographical impact analysis.
   • Social network insights to identify affected communities.
   • Real-time weather data for ongoing risk assessment.
   • Recommendations for resource allocation and expedited claims processing.

By following these steps, you can effectively implement a combined RAG approach to enhance post-disaster claims management, providing more accurate and contextually rich responses.

Conclusion and Next steps​

In conclusion, both Standard RAG and GraphRAG offer unique strengths that can significantly enhance information retrieval and generation tasks. Standard RAG excels in providing concise, document-based summaries, making it ideal for straightforward queries. On the other hand, GraphRAG leverages the power of knowledge graphs to deliver more detailed and interconnected insights, which are particularly valuable in complex scenarios like sales optimization and fraud detection in the financial services sector.



By understanding the distinct capabilities of each approach, organizations can better determine when to use GraphRAG for its rich contextual understanding and when to rely on Standard RAG for quick, relevant summaries. Combining these approaches in an end-to-end copilot application can unlock new levels of efficiency and insight, enabling more informed decision-making and strategic planning. As demonstrated through examples and use cases, the synergy between Standard RAG and GraphRAG can drive innovation and improve outcomes across the industry.

Learn More:​

GraphRAG: Unlocking LLM discovery on narrative private data - Microsoft Research

microsoft/graphrag: A modular graph-based Retrieval-Augmented Generation (RAG) system (github.com)

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