Modern financial electronic exchanges are an exciting and fast-paced
marketplace where billions of dollars change hands every day. They are also
rife with manipulation and fraud. Detecting such activity is a major
undertaking, which has historically been a job reserved exclusively for humans.
Recently, more research and resources have been focused on automating these
processes via machine learning and artificial intelligence. Fraud detection is
overwhelmingly associated with the greater field of anomaly detection, which is
usually performed via unsupervised learning techniques because of the lack of
labeled data needed for supervised learning. 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 financial data. We use exclusive
proprietary limit order book data from the TMX exchange in Montr\'eal, with a
small set of true labeled instances of fraud, to evaluate Deep SAD against its
unsupervised predecessor. We show that incorporating a small amount of labeled
data into an unsupervised anomaly detection framework can greatly improve its
accuracy.

使用深度半监督异常检测技术 Deep SAD，本研究旨在评估其在高频金融数据中检测欺诈行为的有效性，通过利用蒙特利尔的 TMX 交易所的专有限价订单簿数据和少量真实标记的欺诈实例，我们证明将少量标记数据融入无监督异常检测框架可以极大提高其准确性。