Architecting Real-Time Streaming for Generative AI Fraud Detection and Prevention

Last year , we published a reference architecture that demonstrated the integration of streaming data services on AWS with Retrieval Augmented Generation(RAG) in Generative AI applications.

The architecture integrates real-time streaming capabilities with Retrieval Augmented Generation (RAG) for advanced Generative AI applications. It leverages AWS services to capture and process data from various sources, including on-premises and cloud databases, through AWS Database Migration Service and Amazon MSK Connect. The data flows through streaming services like Amazon Kinesis or Amazon MSK, with processing handled by AWS Glue Streaming or Amazon Managed Service for Apache Flink. The architecture incorporates vector databases such as Amazon Aurora PostgreSQL with pgvector for efficient data retrieval, while Amazon SageMaker and Amazon Bedrock power the RAG model. This setup enables real-time data processing, vector similarity search, and dynamic content generation, making it ideal for applications in fraud detection, personalized recommendations, and other time-sensitive use cases in industries like banking and finance.

In this article, we'll explore a critical use case in the banking industry: real-time fraud detection and prevention using streaming data and Retrieval Augmented Generation (RAG) models. We'll examine how financial institutions can harness the power of this architecture to identify and thwart fraudulent activities as they unfold.

Use Case and Implementation Details

Imagine a large international bank processing millions of transactions daily across various channels - credit cards, online banking, ATMs, and mobile apps. Traditionally, fraud detection often relied on batch processing and rule-based systems, leading to delayed responses and potential financial losses. Our proposed architecture revolutionizes this approach. By implementing real-time data streaming, the bank can instantly capture and analyze every transaction. As soon as a customer swipes their card or initiates an online transfer, the data is fed into the system. AWS Database Migration Service or Amazon MSK Connect with Debezium ensures that no transaction goes unnoticed, streaming data from various sources into Amazon Kinesis or Amazon MSK. The streaming data then flows through AWS Glue Streaming or Amazon Managed Service for Apache Flink, where initial processing occurs. This might include basic rule checking, data normalization, and feature extraction.

Here's where the power of RAG comes into play. The processed data is fed into a RAG model hosted on Amazon SageMaker or Amazon Bedrock. This model has been trained on historical transaction data, known fraud patterns, and a vast corpus of financial information. It can quickly retrieve relevant information about the customer's usual spending patterns, compare the current transaction with similar ones in the past, and even consider global fraud trends.

For instance, if a customer who usually makes small local purchases suddenly attempts a large transaction in a foreign country, the RAG model can instantly flag this as potentially suspicious. It doesn't just rely on predefined rules but can generate contextual insights based on the retrieved information. The results of this analysis are then stored in near real-time in Amazon DocumentDB or DynamoDB, allowing for immediate action. If a transaction is flagged as potentially fraudulent, it can be instantly blocked or routed for manual review. The bank's fraud team can access this information through a dashboard, seeing potential fraud attempts as they happen and taking swift action.

Moreover, the system continuously learns and adapts. Each new transaction, whether fraudulent or legitimate, becomes part of the historical data, allowing the RAG model to refine its understanding and improve its accuracy over time. This real-time, AI-driven approach significantly reduces the time between a fraudulent activity occurring and its detection, potentially saving the bank and its customers millions in prevented fraud losses. It also enhances customer experience by reducing false positives and allowing legitimate transactions to proceed without unnecessary interruptions.

Enhancement to the architecture

In addition to the real-time streaming and RAG model, we can further enhance our fraud detection system by leveraging Amazon Bedrock's knowledge base. This integration adds a powerful layer of general and domain-specific knowledge to our fraud detection pipeline.

How it works:

1. Knowledge Ingestion: We can use Amazon Bedrock to create a custom knowledge base that includes:

• Financial regulations and compliance guidelines
• Known fraud patterns and techniques
• Industry best practices for fraud prevention
• Geographic and demographic data relevant to financial transactions

2. Real-time Enrichment: As transactions flow through our system, we can use Amazon Bedrock to enrich the data with relevant information from the knowledge base.
3. Context-Aware Analysis: The RAG model can now leverage this additional context when analyzing transactions, leading to more nuanced and accurate fraud detection.

Benefits of integrating Amazon Bedrock's knowledge base:

1. Enhanced Contextual Understanding: The system can now consider broader contexts beyond just the customer's history. For example, if a transaction occurs in a region known for certain types of fraud, the system can factor this into its risk assessment.
2. Up-to-date Regulatory Compliance: By including current financial regulations in the knowledge base, the system can help ensure that fraud detection practices remain compliant with the latest laws and guidelines.
3. Improved Anomaly Detection: With access to a vast knowledge base of fraud patterns, the system can more easily identify subtle anomalies that might indicate new or evolving fraud techniques.
4. Reduced False Positives: The additional context provided by the knowledge base can help the system better distinguish between unusual but legitimate transactions and truly suspicious activity.
5. Explainable AI: When a transaction is flagged, the system can provide more detailed explanations by referencing specific pieces of knowledge from the Bedrock knowledge base, aiding in the manual review process.
6. Adaptive Learning: As new fraud patterns or regulatory changes emerge, they can be quickly added to the knowledge base, allowing the system to adapt without requiring a full retraining of the RAG model.

Implementation:

To integrate Amazon Bedrock's knowledge base, we would add an additional step in our data processing pipeline:

1. After the initial data processing in AWS Glue or Amazon Managed Service for Apache Flink, we query the Amazon Bedrock knowledge base.
2. Relevant information from the knowledge base is then combined with the transaction data.
3. This enriched data is then passed to the RAG model for analysis.
4. The results, including any insights derived from the knowledge base, are stored in Amazon DocumentDB or DynamoDB for immediate action and future reference.

By incorporating Amazon Bedrock's knowledge base, we create a more intelligent, context-aware fraud detection system. This not only improves the accuracy of fraud detection but also provides richer insights for financial institutions to understand and combat fraudulent activities more effectively. This addition to the architecture leverages Amazon Bedrock's capabilities to create a more comprehensive and adaptable fraud detection system, combining real-time data analysis with broad domain knowledge.

About the author

Jatinder Singh is a Senior Technical Account Manager at AWS who specializes in helping customers with their cloud migration and innovation endeavors. He brings his expertise and enthusiasm for technology to help clients efficiently scale their businesses so that they can focus on their core activities. Outside of work, he enjoys spending moments with his family and indulging in hobbies such as reading, culinary arts, and chess.