Automating Post-trade Compliance Supervision through Machine Learning****

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Canadian financial organizations are required to comply with the regulations specified by the Investment Industry Regulatory Organization of Canada (IIROC) to be able to perform any trading operations in Canada. According to IIROC regulations, investment advisors should be registered in order to make sure they comply with a set of rules related to business conduct, financial operations and trading practices. Consequently, every trade performed by an investment advisor on behalf of a customer must be validated against a set of specific rules. This process is undertaken by a compliance supervisor. Currently, compliance supervisors rely on a risk-based approach using the data generated by a wealth management software in order to detect trades that might violate the rules. However, this approach does not scale with the sheer number of trades made every day. Therefore, a huge number of trades are left unchecked. An automated approach is required in order to perform a comprehensive and exhaustive trade validation. The main objective of this research project is to enable the automation of the post-trade compliance supervision process through a machine learning based approach. ******