Neo4j, a graph intelligence platform used to connect and analyze complex data relationships, has launched Neo4j GraphAware Financial Crime Intelligence, a new graph-native solution designed to help banks and insurers detect, investigate and prevent fraud, money laundering and other financial crimes.
The launch represents Neo4j’s first major product milestone since completing its acquisition of intelligence analysis software company GraphAware in August 2026. The new offering combines Neo4j’s graph technology with GraphAware’s financial crime capabilities to provide investigators with a reusable knowledge layer for enterprise AI and financial crime operations.
Neo4j is targeting a financial crime problem that continues to grow in scale and complexity as fraudulent activity becomes more distributed across accounts, entities, devices and jurisdictions.
The company cited OECD data showing approximately $442 billion was lost to consumer fraud globally in 2025. It also pointed to an Interpol operation in July 2026 that resulted in more than 5,800 arrests across 97 countries and territories, illustrating the highly interconnected nature of modern fraud networks.
At the same time, banks and insurance companies face growing regulatory pressure to proactively detect and prevent financial crime while AI makes fraudulent activity potentially faster, more scalable and more sophisticated.
GraphAware Financial Crime Intelligence is designed around graph technology’s ability to model relationships between people, accounts, transactions, devices, organizations and other entities.
Instead of analyzing records primarily in isolation, graph databases can follow relationships across multiple data points, enabling investigators to uncover patterns that may only become visible when several linked entities or transactions are considered together.
Neo4j said the new platform allows analysts to connect fragmented data sources, develop deeper contextual understanding and use multi-hop reasoning to identify suspicious behavior across complex networks.
The company is positioning the platform as a full-cycle financial crime environment rather than only a fraud detection engine.
Neo4j already supports fraud detection and compliance applications at major financial institutions including BNP Paribas, UBS and Zurich, as well as fintech companies and challenger banks such as Klarna, Prospa and Arhasi.
With GraphAware Financial Crime Intelligence, the company is bringing detection, alerting, investigation and decision-making together on a single graph-native stack supported by a continuously enriched knowledge layer.
The workflow begins with what Neo4j calls Signal, a graph-powered environment that searches connected information for suspicious patterns involving entities, transactions, systems or relationships.
The next stage, Alert, produces investigation-ready alerts intended to provide context around what occurred, how suspicious activity developed and why the behavior represents a potential risk. Neo4j said the approach can also reduce duplicated alerts.
Investigate then uses graph analytics to trace connected information across accounts, transactions and devices. Investigators can add case-specific third-party information where necessary, helping fill information gaps, eliminate false positives and prioritize higher-risk activity.
The final stage, Decide, helps teams take actions such as blocking or declining transactions, escalating cases, filing reports or closing investigations.
Neo4j said the system preserves the relationships and provenance underlying its findings so decisions remain explainable and defensible, while completed investigations can feed back into future monitoring and detection processes.
That explainability is particularly important as financial institutions introduce more AI into fraud and compliance operations.
Banks increasingly need AI systems that can provide not only a risk score or recommendation but also evidence showing why a particular account, transaction or relationship was identified as suspicious.
Neo4j’s strategy is to use a knowledge layer to ground those AI systems in connected enterprise information while maintaining the provenance needed for investigators, regulators and compliance teams to understand the basis of individual findings.
The launch also reflects Neo4j’s broader effort to establish its graph platform as infrastructure for enterprise AI.
The company describes its platform as combining knowledge graphs with graph databases and AI capabilities to turn complex data into usable knowledge for intelligent applications.
Neo4j said its technology is currently used by 84 of the Fortune 100 and supported by a large global graph developer and user community.
GraphAware Financial Crime Intelligence is available now.
KEY QUOTES:
“Every fraud involves a network, every network has a pattern, and those patterns are hiding in your data. Financial crime is a deeply interconnected problem, but one that is better addressed by a modular graph intelligence platform, which, unlike others, natively stores relationships to effortlessly hop between multiple datapoints, detecting suspicious behaviors.”
Michael Down, Global Head of Financial Solutions at Neo4j
