Statistical Detection of Collective Data Fraud

TL;DR

This paper introduces a statistical divergence-based method for detecting collective data fraud by analyzing distribution similarities among data collections, demonstrating its practical applicability in real-world scenarios.

Contribution

It proposes a novel collective detection technique that leverages statistical divergence to identify anomalies in data collections, extending beyond multimedia processing.

Findings

Effective in real-world data fraud detection scenarios

Utilizes distribution similarity for anomaly detection

Applicable across various data domains

Abstract

Statistical divergence is widely applied in multimedia processing, basically due to regularity and interpretable features displayed in data. However, in a broader range of data realm, these advantages may no longer be feasible, and therefore a more general approach is required. In data detection, statistical divergence can be used as a similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Evaluation shows that it is applicable in the real world.