Unlocking Insights: GraphRAG & Standard RAG in Financial Services

GraphRAG

GraphRAG (Graph Retrieval-Augmented Generation) is an advanced approach in natural language processing that integrates and improves upon existing retrieval augmented generation (standard RAG) systems. Knowledge Graph Generated by a large-scale language model (LLM).

This method involves constructing a structured knowledge graph from a set of documents and identifying key entities such as: people, locationand conceptand Expressed as nodes In a graph structure.

These nodes are then clustered. semantic communityIt can generate more comprehensive and diverse answers to complex and multi-hop questions. By leveraging these structured knowledge graphs, GraphRAG significantly improves the quality and relevance of the answers generated.

Differences between Standard RAG and GraphRAG

Standard RAG and GraphRAG differ mainly in the sources and methods of information retrieval.

The standard RAG relies on: Vector storage Retrieves relevant documents based on the user’s query. Ranks and selects top documents, combines them with the query, and uses a language model to generate the final response. GraphRAG, on the other hand, leverages: knowledge graph, What’s Included Entity, Relationship, and Document Graphs. 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 to provide richer, more contextually accurate responses than the document-centric approach of standard RAG.

Difference between standard RAG and graph RAG

Standard RAG and GraphRAG examples

Here is a simple example to show the difference between Standard and GraphRAG. Let’s say you want to know the latest risks in the insurance sector. You stumbled upon a McKinsey article titled “Navigating Changing Risks in the Insurance Industry” and decided to analyze the challenges it presents (Navigating Changing Risks in the Insurance Industry | McKinsey) You may be wondering how Standard RAG and GraphRAG can help you extract information from this article, especially when answering questions like “What are the common themes in the insurance industry?” Using Standard RAG and GraphRAG, you can find information like:

Standard RAG Focus on retrieving and summarizing relevant documents to get more intuitive and concise responses.

In contrast, Graph RAG ~ do Inference on the entire dataset And by leveraging relationships between entities in the knowledge graph, we can provide more detailed and interconnected responses. This can provide richer context by highlighting complex topics such as interconnected risk factors and AI integration in the insurance industry.

Standard RAG Output

GraphRAG Output

How to determine your GraphRAG use case

To determine your GraphRAG (Augmented Search Generation) use case, you must first identify the following areas: Complicated Relationships and Contextual understanding Essential. GraphRAG excels in scenarios where data points are interconnected, such as knowledge management, recommender systems, and fraud detection. Start by mapping entities and their relationships within the domain. For example, in insurance, entities might include policyholders, agents, policies, and claims, while relationships represent policy and claim transactions.

Next, evaluate your potential. Advantages of using graph structures over traditional approaches. GraphRAG can leverage these relationships to enhance data discovery and provide more accurate and contextually relevant information. This approach is particularly useful in domains that require deep insights and nuanced understanding, such as fraud detection, where customer profiles and billing transactions are interconnected. Building a knowledge graph can enable more sophisticated queries and produce richer, more informative responses.

Use cases that leverage the combined capabilities of Standard RAG and GraphRAG

In the financial services sector, integrating Standard RAG and Graph RAG can significantly improve the depth and accuracy of insights. For example, in the banking sector, the combined power of Standard and Graph RAG can help improve the depth and accuracy of insights. Customer Relationship Management (CRM) and Risk Assessment. Standard RAG can bring in vast amounts of customer data, transaction history, and market trends to provide a comprehensive view of your customers’ financial behavior. Meanwhile, Graph RAG can analyze the complex web of relationships between customers, accounts, and transactions to identify potential risks and opportunities. For example, Detects unusual trading patterns It may indicate or emphasize fraudulent activity. Cross-selling opportunities By understanding the interconnected needs of customers, this holistic approach enables banks to deliver more personalized services, improve risk management, and ultimately drive better business performance.

In the insurance industry Billing Processing Leveraging these combined capabilities can be transformative. While standard RAG can efficiently retrieve relevant policy documents, historical claims data, and regulatory guidance, Graph RAG can map relationships between various entities involved in a claim, such as policyholders, healthcare providers, and repair shops. This dual approach not only accelerates the claims review process, but also helps identify Fraudulent claims By discovering hidden connections and patterns that might be missed by traditional methods.

Developing End-to-End Pilot Applications Using a Combined RAG Approach

A step-by-step guide on how to build an end-to-end pilot type application using a combined RAG that includes both standard RAG and graph RAG.

End to End Application Development Using Standard and Graph RAG

1. Use Cases and Data Definition

• Use Cases: Post-disaster claims management.
• data: Past billing history, customer profiles, policy details, disaster impact data, geographic data, social networks, and weather patterns.

2. Create and populate the knowledge graph

• Data collection: Collect data from internal and external sources.
• Data Modeling: Defines a schema for entities and relationships.
• Data collection: Load data into the knowledge graph.

3. Index and embedded data

• Document Index: Related Document Index
• Generating embeddings: Generate embeddings for entities and relationships.

4. Setting up the search system

• Search Documents: We implement a system for retrieving documents from a vector repository.
• Graph Search: Implement graph queries to extract related entities and relationships.

5. Development of ranking and filtering algorithms

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

6. Integration with language models

• Data Combination: Merge information retrieved from two sources.
• Generate response: We use a language model to generate the final response.

7. User Interface Development

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

8. Testing and Verification

• Test Scenario: Verify accuracy and relevance.
• User Feedback: We improve our systems based on feedback.

9. Distribution and Monitoring

• deployment: Deploy to production.
• Monitoring: We continuously monitor and improve.

Workflow Example

1. User query: “Claims Management After Recent Hurricanes”
2. Search Documents: Search past insurance claims history, policy details, and disaster impact reports.
3. Graph Search: Extract geographic data, social networks, and real-time weather data.
4. Ranking and Filtering: Prioritize relevant information.
5. Generate response: Combine data to develop a comprehensive claims management plan.
6. calculation: Please provide a detailed report including:

• Past billing and policy details.
• Geographic impact analysis.
• Social network insights to identify affected communities.
• Real-time weather data for continuous risk assessment.
• Recommendations for resource allocation and expedited claim processing.

Following these steps will help you effectively implement a combined RAG approach to improve claims management following a disaster, providing a more accurate, contextually rich response.

Conclusion and Next Steps

In conclusion, both Standard RAG and GraphRAG offer unique strengths that can significantly improve information discovery and generation tasks. Standard RAG excels at providing concise, document-based summaries, making it ideal for simple queries. GraphRAG, on the other hand, leverages the power of knowledge graphs to provide more detailed, interconnected insights, which are especially valuable in complex scenarios such as sales optimization and fraud detection in the financial services sector.

By understanding the unique capabilities of each approach, organizations can better determine when to use GraphRAG for rich contextual understanding and when to use standard RAG for quick, relevant summaries. Combining these approaches in an end-to-end pilot application unlocks new levels of efficiency and insight, enabling more informed decision making and strategic planning. As demonstrated through case studies and use cases, the synergy between standard RAG and GraphRAG can drive innovation and improve performance across industries.

Learn more:

GraphRAG: Unlocking LLM Discovery for Narrative Personal Data – Microsoft Research

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