Fraud & Risk Detection

Criminals think in networks. You should too.

Real-time graph detection for payment fraud, insurance fraud, and criminal network risk. Memgraph surfaces the connections that transaction-level systems miss.

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The problem

Fraud is a network problem. Single transactions lie.

Single transactions look legitimate

Authorized push payment fraud, staged insurance claims, and synthetic identity fraud all pass individual transaction checks. The pattern only emerges across connected entities.

Fraud doesn't wait for batch processing

Fraudsters operate in real time. Detection that runs overnight on yesterday's data is detection that arrives too late. The window to act is measured in milliseconds.

False positives cost too

Blocking legitimate transactions has a cost: customer experience, chargeback disputes, and operations. Accuracy matters as much as recall.

How Memgraph Helps

Connected data. Real-time decisions.

Three capabilities that change how fraud detection works.

Hybrid models

Fuse graph analytics with machine learning

Graph-powered feature engineering exposes patterns your ML model can't see from transaction data alone: shared devices, account rings, payment path anomalies. Combine both for up to 90% more fraud detected.

Real-time at scale

Millions of transactions per second

In-memory C++ architecture handles high-velocity, write-heavy workloads without degrading under peak load. No batch windows. No stale data. Decisions happen while the transaction is still in flight.

Explainability

Show how the fraud was orchestrated

Graph visualization makes fraud patterns auditable: show investigators, compliance teams, and courts exactly how a fraud network was structured and how it was uncovered.

Use cases

Graph detection works across fraud types.

Card and payment fraud

Detect account takeovers, card-not-present fraud, and authorized push payment scams by tracing connections across accounts, devices, payment paths, and merchant relationships. Patterns invisible in single transactions surface immediately across the graph.

Claims fraud detection

Staged accidents, provider rings, and duplicate claim submissions reveal themselves through shared claimants, witnesses, attorneys, and repair shops. Graph analytics exposes the orchestration behind fraudulent claims and makes the evidence explainable.

Criminal network analysis

Identify hidden relationships between entities (individuals, companies, accounts, jurisdictions) to expose money laundering networks, sanctions evasion, and supply chain risk before regulators or investigators find it first.

Customer storyHow Capitec Built a Graph-Powered Fraud Scoring Pipeline for 3.5M+ Daily Cases

See how Capitec used graph-powered fraud scoring to process 3.5M+ daily records, uncover hidden scam connections, and expand from one live graph to seven.Read story

Capabilities

Why Memgraph.

Sub-millisecond traversals. Native vector search. Real-time graph updates. Built for knowledge graphs at production scale.

Fuse graph analytics with machine learning
Graph-powered feature engineering exposes patterns your ML model can't see from transaction data alone: shared devices, account rings, payment path anomalies. Combine both for up to 90% more fraud detected.
Millions of transactions per second
In-memory C++ architecture handles high-velocity, write-heavy workloads without degrading under peak load. No batch windows. No stale data. Decisions happen while the transaction is still in flight.
Show how the fraud was orchestrated
Graph visualization makes fraud patterns auditable: show investigators, compliance teams, and courts exactly how a fraud network was structured and how it was uncovered.

Resources