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

加拿大金融机构必须遵守加拿大投资行业监管组织(IIROC)规定的法规，才能在加拿大开展任何交易业务。根据IIROC的规定，投资顾问应该注册，以确保他们遵守一套与商业行为、金融运营和交易实践有关的规则。因此，投资顾问代表客户进行的每一笔交易都必须根据一套特定的规则进行验证。这一过程由合规监督员负责。目前，合规监管者依赖一种基于风险的方法，使用财富管理软件生成的数据来检测可能违反规则的交易。然而，这种方法并不能完全适应每天的交易量。因此，大量的交易没有受到检查。为了进行全面和详尽的贸易验证，需要一种自动化的方法。这项研究项目的主要目标是通过基于机器学习的方法实现交易后合规监管过程的自动化。******

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. ******