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

支持自动化和全面的交易后监管合规性的领域和学习模型

• 批准号: 549805-2020
• 负责人: Gherbi, Abdelouahed
• 金额: $ 2.18万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706109
• 关键词: Domain; Learning; Models; Support; Automated

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.

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，投资顾问应进行注册，以确保他们遵守一系列与商业行为、财务运作和交易惯例有关的规则。因此，投资顾问代表客户进行的每笔交易都应遵守IIROC的规定。这一过程由合规主管负责。目前，合规监管机构依靠基于风险的方法，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，这种方法并不适用于每天交易的数量。此外，IIROC监管专业知识不足，IIROC监管者的影响也不统一。 需要有一种办法来进行系统、全面和及时的贸易验证。本研究项目的目标是实现贸易合规监督过程的自动化。为了实现这一目标，该项目将探索如何将使用模型驱动工程构建的IIROC领域模型与先进的机器学习技术相结合。