Deep Unsupervised Anomaly Detection in Options Markets

In the last few years, a high increase in the interest of traders and investors towards financial instruments directly lead to an important augmentation of the information received daily by exchanges. Exchanges 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 markets activity. In this project, we set to design a new artificial intelligence algorithm that will detect anything in the Montreal exchange’s market that seems abnormal or fraudulent, 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.

Intern: 
Cédric Poutré;Timothy DeLise;Pan Liu
Superviseur universitaire: 
Manuel Morales;Gilles Caporossi
Province: 
Quebec
Partenaire: 
Partner University: 
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