Generate synthetic counterparty (CR) risk data with generative AI using Amazon Bedrock LLMs and RAG

This AWS Machine Learning Blog article demonstrates how to generate synthetic counterparty risk (CR) data using generative AI and Retrieval Augmented Generation (RAG) with Amazon Bedrock's Large Language Models.

• Uses Amazon Titan Text Embeddings and Anthropic's Claude Haiku for data generation
• Focuses on creating synthetic OTC (over-the-counter) derivatives counterparty risk data
• Implements a three-step process: data indexing, data generation, and data validation
• Utilizes Chroma vector database for efficient data embedding and retrieval
• Validates generated data using Q-Q plots and correlation heat maps

The solution provides a method for financial institutions to generate realistic synthetic training data for machine learning models, addressing challenges in collecting real-world counterparty risk data.