Deep Semi‑Supervised Anomaly Detection for Finding Fraud in the Futures Market
"#frauddetection is overwhelmingly associated with the greater field of #anomalydetection, which is usually performed via unsupervised learning techniques because of the lack of labeled data needed for #supervisedlearning. However, a small quantity of labeled data does often exist. This research article aims to evaluate the efficacy of a deep semi‑supervised anomaly detection technique, called Deep SAD, for detecting #fraud in high‑frequency #financialdata."