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Easily build a knowledge graph from your existing documents with Document Intelligence. October 6, 2026 Today, Neo4j, Inc. is announcing the upcoming release to general availability (GA) of Document Intelligence in Neo4j AuraDB on the Free, Professional, and Business Critical tiers. With Document Intelligence, you can build a knowledge graph from existing documents, including plain text files, PDFs, and Microsoft Word files, without writing a single line of code. Documents that previously sat unused and unqueried can now be searched and acted upon. Documents hold the context behind many business decisions. Contracts describe obligations, reports record risks, and policy documents explain how a business operates. The connections between those documents matter. A question about a supplier may depend on a contract, an exception recorded elsewhere, and the products that the supplier provides. A knowledge graph makes those relationships explicit, so an application can follow them across documents and into the business data you already maintain. Getting started is easy. You describe what you want to extract, then refine the graph model through the canvas or built-in assistant. The resulting graph provides applications and AI agents with a cleaner signal than raw text alone, helping them retrieve related context with fewer tokens. When Neo4j, Inc. introduced Document Intelligence in preview, Neo4j, Inc. brought document processing, graph modeling, and extraction into the Aura console. GA builds on that foundation with support for larger collections, richer document content, and deeper integration with the graph you already have. Document Intelligence can now process hundreds of documents in managed background jobs, interpret information in images and tables, extend an existing graph model, and resolve extracted entities against data already in AuraDB. You can inspect and refine the graph model on the canvas or with the built-in assistant before import. What changes with the GA release? Together, these changes make document-to-graph extraction a repeatable workflow for larger, evolving collections. Larger collections and richer documents Document Intelligence now processes hundreds of documents per job. Imports run in the background, with job status available in the Aura console, and retries make longer-running jobs more resilient. A collection of contracts or reports can serve as the starting point for an entity graph that connects information across the entire collection. Neo4j, Inc. has also expanded the content that Document Intelligence can understand. Alongside text, it can interpret images and tables so their content can inform graph extraction. This brings more of a document into the resulting graph, including information that would otherwise remain inside visual elements. Build on the graph you already have New documents often add information about entities you already know. Document Intelligence can extend an existing graph model, resolve extracted entities against records already in AuraDB, and add new facts and relationships to those records. For example, contracts can add obligations to a supplier graph that already connects companies to the products they supply. Entity resolution determines when different mentions refer to the same real-world entity. Within each job, Document Intelligence normalizes entities, narrows the pairs worth comparing, scores the remaining candidates, and merges those above a similarity threshold. It then compares the results with nodes already in AuraDB. More control through the interactive assistant The assistant can generate and iteratively refine a graph model from a sample of your documents, start imports, and report job status. During the current session, it maintains the context of your conversation so you can develop the graph model through ongoing discussion. Descriptions and typed properties make the proposed model easier to review and more precise for extraction. Sampling across the collection helps the proposed model reflect recurring entities and relationships rather than the contents of a single document. You can ask the assistant to change a relationship, add information found in the sampled documents, or check whether the proposed graph model can answer your business questions. You can then inspect the result on the canvas. Conversation and direct editing work on the same graph model, keeping your extraction choices visible throughout the session. See Document Intelligence in action Here's a short walkthrough, from adding documents to starting an import. It shows the assistant proposing a graph model, helping you refine it on the canvas, and starting a background job that writes the resulting graph to AuraDB. You can follow the job's status in the Aura console while it runs. Connect the entity graph to its lexical graph Document Intelligence works in two stages. First, it samples the document collection to propose a graph model for you to review and refine. Once the model is ready, it processes the full collection, extracts entities and relationships, resolves repeated mentions, and writes the resulting graph to AuraDB. The result is one graph with connected entities and lexical layers. The lexical layer contains document and chunk nodes, with embeddings stored on chunks for similarity search. Extracted entities link back to the chunks in which they were found, while relationships between entities follow the graph model you reviewed. Applications can traverse those relationships and retrieve the source passages behind a result. Start building with your documents Document Intelligence is available in the Aura console across Free, Professional, and Business Critical. Bring your documents, describe the information you need, refine the proposed graph model, and review a sample of the extracted graph before starting the full import as a background job. The Document Intelligence documentation covers the supported sources and the workflow in detail. What comes next Next, Neo4j, Inc. is extending Document Intelligence beyond the Aura console with API, command-line, and Model Context Protocol access for pipelines and agents. Neo4j, Inc. is also exploring graph models that start from a business description, clearer job feedback and cancellation, more control over entity resolution, re-imports from updated sources, and visual tools for testing results. Broader enterprise deployment options, including customer cloud environments and self-managed Neo4j, are also on the roadmap. As you put Document Intelligence to work, use the Feedback button in the Aura console to tell Neo4j, Inc. what would make the biggest difference. Your feedback will help shape what Neo4j, Inc. build next

Neo4j has launched GraphAware Financial Crime Intelligence, a graph-native solution for detecting, investigating, and preventing financial crime in banking and insurance. The announcement follows Neo4j's acquisition of GraphAware in August 2026. The OECD reported $442 billion was lost to consumer fraud globally in 2025, making it the world's second-most common crime after burglary. Financial institutions face increasing regulatory pressure and expensive fines to prevent such crimes. The new solution uses graph databases to connect data silos and identify patterns through multi-hop reasoning, enabling analysts to make faster decisions. Neo4j already provides fraud detection solutions for major financial institutions including BNP Paribas, UBS, and Zurich. The platform offers a complete financial crime investigation cycle built on a knowledge layer for AI, addressing fraud, anti-money laundering, and compliance challenges.

Neo4j intros graph-based suite for fighting financial crime. The vendor is adding tooling from its GraphWise acquisition to go beyond fraud detection and differentiate itself from those providing only part of the crime-stopping ecosystem. Published: 16 Sep 2026 Graph database specialist Neo4j is upping its fight against financial crime. Just over one month after completing the acquisition of GraphAware, which was first agreed upon in June, Neo4j launched a new suite for detecting and stopping fraud that includes capabilities it inherited through the purchase. Neo4j GraphAware Financial Crime Intelligence is a graph-native tool featuring a graph-based knowledge layer purpose-built for banks and insurance companies to use AI to detect and prevent financial crime. Neo4j has provided fraud detection capabilities since launching its first graph database in 2010. However, after detecting potential incidents, customers had to carry out their investigations and analyses using other platforms. GraphAware Financial Crime Intelligence, which combines Neo4j's existing graph-based fraud detection capabilities with complementary capabilities from GraphAware, encompasses the entire financial crime investigation cycle from detection through alerting and investigation to decision. "Detection was only ever half the job," Devin Pratt, an analyst at IDC, told TechTarget. "Putting the full investigation on one graph-native stack removes the handoffs - and the risk - that live in the gaps between systems." Stephen Catanzano, an analyst at Omdia, a division of TechTarget, likewise noted that the significance of Financial Crime Intelligence is that it expands on Neo4j's fraud detection capabilities. "What makes it particularly valuable is the addition of a comprehensive workflow... underpinned by a reusable knowledge layer for trustworthy AI that enables explainable decision-making throughout the entire detection, investigation and prevention process," he told TechTarget. A complete crime-fighting lifecycle. Neo4j is not the only graph database specialist to offer fraud detection capabilities. TigerGraph, PuppyGraph and Memgraph do as well. In addition, broader-based data management vendors such as AWS and Oracle provide fraud detection tools as part of their database offerings. However, Catanzano noted that Neo4j is going beyond what most competing vendors offer by combining fraud detection with investigative and decision-making capabilities in a single feature. "While other vendors may offer fraud detection tools, Neo4j's approach of combining graph-native storage with a reusable knowledge layer that continuously enriches context and enables multi-hop reasoning across the entire investigation workflow positions it distinctively in the market," he said. Pratt similarly stated that Neo4j's differentiation lies in the depth and completeness of Financial Crime Intelligence. In particular, he highlighted the inclusion of an explainable knowledge layer that provides a map of connections between data points and shows how and why the tool's AI capabilities deemed something suspicious and made the recommendations it did. "Plenty of vendors do graph, or do fraud," Pratt said. "Neo4j's bet is owning the whole workflow, with explainability built in." While acquiring GraphAware enabled Neo4j to build a complete financial crimes detection suite, the vendor's impetus for developing Financial Crime Intelligence was also a response to the growing need for customers to show they are taking appropriate steps to fight fraud, according to Michael Down, Neo4j's global head of financial solutions. "With the acquisition, we have the investigation and knowledge layer capabilities to package a... decision workflow, rather than requiring institutions to build it themselves," he told TechTarget. "The timing reflects both the acquisition coming together and a market under real pressure, with regulators pushing harder on institutions to prove proactive prevention, not just after-the-fact detection." Yearend plans. Neo4j's product development plans over the final months of 2026 include expanding and improving its knowledge layer for AI, adding agentic AI capabilities to its platform to aid users, and enabling customers to remain compliant with data sovereignty regulations by enabling them to control where and how their data models run, according to Down. "Financial Crime Intelligence is an early proof point of this approach [to data sovereignty], applying what we built with GraphAware to a specific vertical," he said. "We expect the same pattern to extend into other industries over time." Turning its knowledge layer into a foundation for other industry-specific capabilities is a wise strategy, according to Pratt, who noted that financial services is not the only vertical that benefits from explainable decisions based on connected data. "Build the knowledge layer once for fraud, and the payback comes from every regulated decision it can serve next," he said. Catanzano, meanwhile, suggested that Neo4j could improve its fraud detection and financial crime fighting prowess by adding AI capabilities such as predictive risk scoring and investigation recommendations to help analysts battle growing AI-powered fraud tactics. In addition, like Pratt, he advised Neo4j to expand crime detection capabilities beyond financial services. "Neo4j might extend the platform to regulated industries facing similar networked crime challenges, such as healthcare fraud or supply chain integrity, thereby appealing to new market segments while deepening its position as the go-to graph intelligence platform for complex, relationship-driven risk detection across enterprises," he said. Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management. Related resources.