Fraud Detection in Derivatives Market using Deep Unsupervised Anomaly Detection and NLP
In the last few years, a high increase in the interest of traders and investors towards financial instruments directly led to an important augmentation of the information received daily by exchanges. Exchange regulators, who constantly monitor markets to unveil potential infractions, traditionally perform their investigation manually and the notable growth in market activity represents an important risk of fraudulent events going unnoticed. In response to that new reality, exchanges around the globe are establishing automated surveillance systems that track market activity. In this project, we set out to design new artificial intelligence algorithms that will detect anything in the Montreal exchange’s market that seems abnormal or fraudulent in the market data and news, so that analysts can focus on these alerts. Such a system could potentially detect fraudulent cases that are currently going unnoticed, while drastically reducing human costs and validation time.
View Full Project DescriptionManuel Morales;Gilles Caporossi
Bourse de Montréal
Mathematics
Finance and Insurance
HEC Montréal; Université de Montréal
Accelerate