Domain and Learning Models to Support Automated and Comprehensive Post-trade Regulation Compliance

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应遵守IIROC的规定。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。此外，IIROC监管方面的专门知识不足，IIROC主管的影响也不一致。需要有一种方法来进行系统、全面和及时的贸易验证。这项研究项目的目标是实现交易合规监管过程的自动化。为了实现这一目标，本项目将探索如何将使用模型驱动工程建立的IIROC领域模型与先进的机器学习技术相结合。

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 should comply with the IIROC regulations. 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 on a daily basis. In addition, there is a shortage in terms IIROC regulation expertise and the impact of IIROC supervisor is not uniform. There is a need for an approach to perform a systematic, comprehensive and timely trade validation. The objective of this research project is to enable the automation of the trade compliance supervision process. In order to achieve this objective, this project will explore how to integrate a domain model for IIROC built using a model-driven engineering with advanced machine learning techniques.