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

TL;DR

This paper evaluates a deep semi-supervised anomaly detection method, Deep SAD, for identifying fraud in high-frequency financial trading data, demonstrating improved accuracy with limited labeled examples.

Contribution

It introduces the application of Deep SAD to financial fraud detection, showing how small labeled datasets enhance unsupervised anomaly detection performance.

Findings

Deep SAD outperforms unsupervised methods in fraud detection accuracy.

Incorporating limited labeled data significantly improves detection results.

The approach is validated on proprietary limit order book data from TMX exchange.

Abstract

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.