Fraud Detection in Derivatives Market using Generative Adversarial Networks

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, 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.

Faculty Supervisor:

Manuel Morales

Student:

Partner:

Bourse de Montréal

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

Université de Montréal

Program: