How can data analytics help detect fraud patterns?

How can data analytics help detect fraud patterns?

• A. It detects only deliberate insider trading.
• B. It replaces the need for human investigators.
• C. It identifies anomalies, duplicate payments, unusual vendor activity, and outliers.
• D. It cannot detect fraud patterns.

Answer: (C)

Data analytics uses large datasets to spot deviations from normal activity. It can identify anomalies that don’t fit past patterns, flagging those for review. It helps catch duplicate payments by cross-checking invoices, payments, and vendors to find repeats. It highlights unusual vendor activity, such as a sudden spike in spending with a supplier or the appearance of new vendors with unexpectedly high volumes. It also detects outliers—transactions that sit far from the established baseline. By combining these signals, analytics provides investigators with concrete leads to follow, making fraud detection more efficient across big data. It doesn’t replace human investigators and isn’t limited to insider trading; it can detect a range of fraud patterns, which is why this option is the best fit.

Data analytics uses large datasets to spot deviations from normal activity. It can identify anomalies that don’t fit past patterns, flagging those for review. It helps catch duplicate payments by cross-checking invoices, payments, and vendors to find repeats. It highlights unusual vendor activity, such as a sudden spike in spending with a supplier or the appearance of new vendors with unexpectedly high volumes. It also detects outliers—transactions that sit far from the established baseline. By combining these signals, analytics provides investigators with concrete leads to follow, making fraud detection more efficient across big data. It doesn’t replace human investigators and isn’t limited to insider trading; it can detect a range of fraud patterns, which is why this option is the best fit.