Fraud detection with SurrealDB

Financial fraud is a rapidly increasing global issue, significantly impacting both consumer and business sectors, with recent studies indicating that fraud now accounts for 6.5% of annual revenue losses for companies, totaling $359 billion in losses for a sample set, and $485 billion globally in 2023. Fraud is often a graph problem, as it involves complex relationships between entities such as shared emails and reused devices, which can be better understood through graph databases. SurrealDB offers a comprehensive platform to tackle fraud detection by integrating graph, document, and vector data, facilitating real-time alerts, event-based actions, and inline machine learning scoring, all within a single system. This unified approach simplifies the architecture by eliminating the need for multiple external services, thereby reducing maintenance complexity, latency, and costs. SurrealQL, the query language of SurrealDB, is designed to model frequent fraud patterns efficiently, allowing for rapid adoption by engineers familiar with SQL. The platform's real-time capabilities are enhanced by SurrealML, which enables the execution of machine learning models directly within the database, providing immediate fraud predictions and enabling a live feedback loop for continuous model improvement.