Operationalizing Enterprise Knowledge Graphs

Use Case: Fraud Detection

Use Case Enterprise Knowledge Graphs

Adaptive Fraud Detection & Analytics with ArangoDB

Fraud is a growing problem across industries and national borders. In 2019, organizations lost over $5 trillion worldwide and spent additional $3.13 in remediation costs for each dollar of fraud. While losing cash is the obvious problem, many organizations struggle even more with the damage fraud does to their well-known brands.

Today’s criminals are constantly coming up with new techniques to hide their activities by forming fraud networks with stolen or synthetic identities. In many cases, attacks are launched from multiple vectors and can only be discovered by connecting diverse data sources to uncover difficult-to-detect patterns.

Fraud detection is a multi-dimensional prolem

Leading brands across industries use ArangoDB’s multi-model graph capabilities to detect various fraud patterns in large data streams by looking beyond individual data points and understanding the context behind them.

The flexibility of multi-model combined with the rich graph pattern detection capabilities built into ArangoDB are an optimal companion. Detect today’s and tomorrow’s fraud activities in real time and adapt quickly to evolving patterns in the future.

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