The text discusses the integration of large language models (LLMs) and graph databases to transform static risk assessment into a dynamic data-driven strategy in commercial credit risk assessment. The current process involves analyzing financial statements, leveraging ratios, and assessing industry and business model factors, which are then fed into internal risk rating models. However, much of this information is stored as unstructured data, introducing complexity and challenges such as bias in data interpretation and overlooking critical factors due to human error. Large language models can extract vast amounts of unstructured data and organize it into a database for faster analysis. Graph databases can be used to represent complex relationships between entities and are better suited than pure vector databases for this task. The text also introduces the concept of GraphRAG, a retriever that combines vector search and Cypher traversals to incorporate additional nodes and relationships, resulting in more accurate answers. This approach enables the creation of a smarter brain that thinks like an analyst, connecting the dots between seemingly unrelated disclosures, commodities reports, and environmental events to surface insights that traditional models might miss entirely.