Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures
  Market
Timothy DeLise
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.