Why 30% to 40% of Time Spent Searching for Data Hurts Fraud Operations

Fraud operations are hampered by the fragmented data context, which leads to inefficiencies and delays in detecting risky patterns, contributing significantly to global financial losses, as evidenced by the $485.6 billion lost to fraud scams in 2023. Despite having vast data resources, fraud teams struggle because critical contextual information is dispersed across various disconnected systems, requiring analysts to manually piece together necessary insights. The challenge lies not in the volume of data but in the lack of integration and the ability to identify connections and relationships across data points, which are crucial for understanding the full scope of potential fraud. Traditional systems are ill-equipped for network analysis, which is essential for identifying complex fraud patterns that emerge through interconnected entities. Leveraging graph technology can enhance fraud detection by enabling relationship-driven data retrieval, allowing analysts to query the network of relationships and identify multi-hop fraud risks more efficiently. This approach shifts the focus from isolated events to connected behaviors, making it easier to detect and investigate fraud, thereby empowering analysts with the necessary context for informed decision-making.

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