Financial fraud detection isn’t just important to banks—it’s essential. With global fraud losses surpassing $40 billion annually and sophisticated criminal networks constantly evolving their tactics, financial institutions face an increasingly complex threat landscape. Today’s fraud schemes operate across multiple accounts, institutions, and channels, creating intricate webs designed specifically to evade detection systems.
Financial institutions have invested heavily in detection capabilities, but the core challenge remains: how to connect the dots across fragmented information landscapes where the evidence of fraud exists not within individual documents or transactions, but in the relationships between them.
In this post, we show how to use Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics to build a financial fraud detection solution.
The limitations of traditional RAG systems
In recent years, Retrieval Augmented Generation (RAG) has emerged as a promising approach for building AI systems grounded in organizational knowledge. However, traditional RAG-based systems have limitations when it comes to complex financial fraud detection.The fundamental limitation lies in how conventional RAG processes information. Standard RAG retrieves and processes document chunks as isolated units, looking for semantic similarities between a query and individual text passages. This approach works well for straightforward information retrieval, but falls critically short in the following scenarios:
Evidence is distributed across multiple documents and systems
The connections between entities matter more than the entities themselves
Complex relationship chains require multi-hop reasoning
Structural context (like hierarchical document organization) provides critical clues
Entity resolution across disparate references is essential
A fraud analyst intuitively follows connection paths—linking an account to a phone number, that phone number to another customer, and that customer to a known fraud ring. Traditional RAG systems, however, lack this relational reasoning capability, leaving sophisticated fraud networks undetected until losses have already occurred.
Amazon Bedrock Knowledge Bases with GraphRAG for financial fraud detection
Amazon Bedrock Knowledge Bases GraphRAG helps financial institutions implement fraud detection systems without building complex graph infrastructure from scratch. By offering a fully managed service that seamlessly integrates knowledge graph construction, maintenance, and querying with powerful foundation models (FMs), Amazon Bedrock Knowledge Bases dramatically lowers the technical barriers to implementing relationship-aware fraud detection. Financial organizations can now use their existing transaction data, customer profiles, and risk signals within a graph context that preserves the critical connections between entities while benefiting from the natural language understanding of FMs. This powerful combination enables fraud analysts to query complex financial relationships using intuitive natural language to detect suspicious patterns that can result in financial fraud.
Example fraud detection use case
To demonstrate this use case, we use a fictitious bank (AnyCompany Bank) in Australia whose customers hold savings, checking, and credit card accounts with the bank. These customers perform transactions to buy goods and services from merchants across the country using their debit and credit cards. AnyCompany Bank is looking to use the latest advancements in GraphRAG and generative AI technologies to detect subtle patterns in fraudulent behavior that will yield higher accuracy and reduce false positives.A fraud analyst at AnyCompany Bank wants to use natural language queries to get answers to the following types of queries:
Basic queries – For example, “Show me all the transactions processed by ABC Electronics” or “What accounts does Michael Green own?”
Relationship exploration queries – For example, “Which devices have accessed account A003?” or “Show all relationships between Jane Smith and her devices.”
Temporal pattern detection queries – For example, “Which accounts had transactions and device access on the same day?” or “Which accounts had transactions outside their usual location pattern?”
Fraud detection queries – For example, as “Find unusual transaction amounts compared to account history” or “Are there any accounts with failed transactions followed by successful ones within 24 hours?”
Solution overview
To help illustrate the core GraphRAG principles, we have simplified the data model to six key tables: accounts, transactions, individuals, devices, merchants, and relationships. Real-world financial fraud detection systems are much more complex, with hundreds of entity types and intricate relationships, but this example demonstrates the essential concepts that scale to enterprise implementations. The following figure is an example of the accounts table.
The following figure is an example of the individuals table.
The following figure is an example of the devices table.
The following figure is an example of the transactions table.
The following figure is an example of the merchants table.
The following figure is an example of the relationships table.
The following diagram shows the relationships among these entities: accounts, individuals, devices, transactions, and merchants. For example, the individual John Doe uses device D001 to access account A001 to execute transaction T001, which is processed by merchant ABC Electronics.
In the following sections, we demonstrate how to upload documents to Amazon Simple Storage Service (Amazon S3), create a knowledge base using Amazon Bedrock Knowledge Bases, and test the knowledge base by running natural language queries.
Prerequisites
To follow along with this post, make sure you have an active AWS account with appropriate permissions to access Amazon Bedrock and create an S3 bucket to be the data source. Additionally, verify that you have enabled access to both Anthropic’s Claude 3.5 Haiku and an embeddings model, such as Amazon Titan Text Embeddings V2.
Uplaod documents to Amazon S3
In this step, you create an S3 bucket as the data source and upload the six tables (accounts, individuals, devices, transactions, merchants, and relationships) as Excel data sheets. The following screenshot shows our S3 bucket and its contents.
Create a knowledge base
Complete the following steps to create the knowledge base:
On the Amazon Bedrock console, choose Knowledge Bases under Builder tools in the navigation pane.
Choose Create and Knowledge Base with vector store.
In the Knowledge Base details section, provide the following information:
Enter a meaningful name for the knowledge base.
For IAM permissions, select Create and use a new service role to create a new AWS Identity and Access Management (IAM) role.
For Choose data source, select Amazon S3.
Choose Next.
In the Configure data source section, provide the following information:
Enter a data source name.
For Data source location, select the location of your data source (for example, we select This AWS account).
For S3 source, choose Browse S3 and choose the location where you uploaded the files.
For Parsing staretgy, select Amazon Bedrock default parser.
For Chunking strategy, choose Default chunking.
Choose Next.
In the Configure data storage and processing section, provide the following information:
For Embeddings model, choose Titan Text Embeddings V2.
For Vector store creation method, select Quick create a new vector store.
For Vector store type, select Amazon Neptune Analytics (GraphRAG).
Choose Next
Amazon Bedrock chooses the FM as Anthropic’s Claude 3 Haiku v1 to automatically build graphs for our knowledge base. This automatically enables contextual enrichment.
Choose Create knowledge base.
Choose the knowledge base when it’s in Available status.
Select the data source and choose Sync, then wait for the sync process to complete.
In the sync process, Amazon Bedrock ingests data files from Amazon S3, creates chunks and embeddings, and automatically extracts entities and relationships, creating the graph.
Test the knowledge base and run natural language queries
When the sync is complete, you can test the knowledge base.
In the Test Knowledge Base section, choose Select model.
Set the model as Anthropic’s Claude 3.5 Haiku (or another model of your choice) and then choose Apply.
Enter a sample query and choose Run.
Let’s start with some basic queries, such as “Show me all transactions processed by ABC Electronics” or “What accounts does Michael Green own?” The generated responses are shown in the following screenshot.
We can also run some relationship exploration queries, such as “Which devices have accessed account A003?” or “Show all relationships between Jane Smith and her devices.” The generated responses are shown in the following screenshot. To arrive at the response, the model will do multi-hop reasoning where it will traverse multiple files.
The model can also perform temporal pattern detection queries, such as “Which accounts had transactions and device access on the same day?” or “Which accounts had transactions outside their usual location pattern?” The generated responses are shown in the following screenshot.
Let’s try out some fraud detection queries, such as “Find unusual transaction amounts compared to account history” or “Are there any accounts with failed transactions followed by successful ones within 24 hours?” The generated responses are shown in the following screenshot.
The GraphRAG solution also enables complex relationship queries, such as “Show the complete path from Emma Brown to Pacific Fresh Market” or “Map all connections between the individuals and merchants in the system.” The generated responses are shown in the following screenshot.
Clean up
To avoid incurring additional costs, clean up the resources you created. This includes deleting the Amazon Bedrock knowledge base, its associated IAM role, and the S3 bucket used for source documents. Additionally, you must separately delete the Neptune Analytics graph that was automatically created by Amazon Bedrock Knowledge Bases during the setup process.
Conclusion
GraphRAG in Amazon Bedrock emerges as a game-changing feature in the fight against financial fraud. By automatically connecting relationships across transaction data, customer profiles, historical patterns, and fraud reports, it significantly enhances financial institutions’ ability to detect complex fraud schemes that traditional systems might miss. Its unique capability to understand and link information across multiple documents and data sources proves invaluable when investigating sophisticated fraud patterns that span various touchpoints and time periods.For financial institutions and fraud detection teams, GraphRAG intelligent document processing means faster, more accurate fraud investigations. It can quickly piece together related incidents, identify common patterns in fraud reports, and connect seemingly unrelated activities that might indicate organized fraud rings. This deeper level of insight, combined with its ability to provide comprehensive, context-aware responses, enables security teams to stay one step ahead of fraudsters who continuously evolve their tactics.As financial crimes become increasingly sophisticated, GraphRAG in Amazon Bedrock stands as a powerful tool for fraud prevention, transforming how you can analyze, connect, and act on fraud-related information. The future of fraud detection demands tools that can think and connect like humans—and GraphRAG is leading the way in making this possible.
About the Authors
Senaka Ariyasinghe is a Senior Partner Solutions Architect at AWS. He collaborates with Global Systems Integrators to drive cloud innovation across the Asia-Pacific and Japan region. He specializes in helping AWS partners develop and implement scalable, well-architected solutions, with particular emphasis on generative AI, machine learning, cloud migration strategies, and the modernization of enterprise applications.
Senthil Nathan is a Senior Partner Solutions Architect working with Global Systems Integrators at AWS. In his role, Senthil works closely with global partners to help them maximize the value and potential of the AWS Cloud landscape. He is passionate about using the transformative power of cloud computing and emerging technologies to drive innovation and business impact.
Deependra Shekhawat is a Senior Energy and Utilities Industry Specialist Solutions Architect based in Sydney, Australia. In his role, Deependra helps energy companies across the Asia-Pacific and Japan region use cloud technologies to drive sustainability and operational efficiency. He specializes in creating robust data foundations and advanced workflows that enable organizations to harness the power of big data, analytics, and machine learning for solving critical industry challenges.
Aaron Sempf is Next Gen Tech Lead for the AWS Partner Organization in Asia-Pacific and Japan. With over 20 years in distributed system engineering design and development, he focuses on solving for large-scale complex integration and event-driven systems. In his spare time, he can be found coding prototypes for autonomous robots, IoT devices, distributed solutions, and designing agentic architecture patterns for generative AI-assisted business automation.
Ozan Eken is a Product Manager at AWS, passionate about building cutting-edge generative AI and graph analytics products. With a focus on simplifying complex data challenges, Ozan helps customers unlock deeper insights and accelerate innovation. Outside of work, he enjoys trying new foods, exploring different countries, and watching soccer.
JaiPrakash Dave is a Partner Solutions Architect working with Global Systems Integrators at AWS based in India. In his role, JaiPrakash guides AWS partners in the India region to design and scale well-architected solutions, focusing on generative AI, machine learning, DevOps, and application and data modernization initiatives.