Neo4j has launched GraphAware Financial Crime Intelligence, a graph-based detection and investigation solution for banks and insurers.
Fraud ranks as one of the most common global crimes, second only to burglary, according to the OECD, which found that consumer fraud losses reached USD 442 billion worldwide in 2025. The problem is also distributed across borders and networks: Interpol reported over 5.800 arrests across 97 countries and territories following a single global fraud operation in July 2026, which also intercepted USD 293 million. Regulatory scrutiny has increased in parallel, with the US Financial Crimes Enforcement Network (FinCEN) proposing reforms to financial institution compliance programmes that could bring higher penalties. Combined with the growing use of artificial intelligence by fraudsters to scale and diversify attacks, these pressures are prompting banks and insurers to reassess how they detect, investigate, and report financial crime.
A graph-native approach to detection
GraphAware Financial Crime Intelligence is built on graph database technology, which stores relationships between data points natively rather than inferring them through joins across separate tables. According to Neo4j, this allows analysts and investigators to connect previously siloed data, build broader context around suspicious activity, and trace patterns through multi-hop connections between entities, transactions, systems, and relationships.
Michael Down, Global Head of Financial Solutions at Neo4j, said financial crime is inherently networked, with patterns often hidden within existing data, and that a graph-based platform is positioned to surface such patterns by tracing relationships across multiple data points.
Covering the full investigation cycle
The new solution is structured around four stages: Signal, which detects suspicious activity hidden within connected data, Alert, which generates investigation-ready alerts with contextual information on what is involved and why it is flagged, Investigate, which uses graph analytics to trace linked data across accounts, transactions, and devices while incorporating third-party data where needed, and Decide, which supports outcomes such as blocking, declining, escalating, reporting, or closing a case, with each decision intended to be explainable and retained as longer-term evidence for ongoing monitoring.
Existing footprint in financial services
Neo4j already supports fraud detection and compliance use cases at financial institutions, including BNP Paribas, UBS, and Zurich, as well as fintechs and challenger banks such as Klarna, Prospa, and Arhasi. With GraphAware Financial Crime Intelligence, the company is consolidating detection, investigation, and prevention into a single graph-native stack, positioned as a continuously updated environment underpinned by a shared knowledge layer intended to support AI-driven analysis across the financial crime lifecycle.
