Join the National Bank of Canada Team!
Founded in 1859, the National Bank of Canada provides financial services to individuals, businesses, institutional clients and governments across Canada. It is one of six systemically important banks in Canada and one of the most profitable banks in the world based on return on equity.
There are three sectors in Canada: Personal and Commercial, Wealth Management and Financial Markets. A fourth Sector, Specialized Financing in the United States and International, complements the growth of the domestic activities.
Industry : Bank
Founded : 1859
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Data security and confidentiality are at the heart of organizations’ concerns today. The challenge is to find a compromise between valuing data and the risk of using personal data for analytical and artificial intelligence projects.
Data governance shares the responsibility with other teams, namely with cybersecurity experts, to put in place the necessary measures to maintain granular control over the access rights of each employee. For example, it defines access levels by role directly on the data. However, the implementation of these measures and the resulting processes restricts access to the data, regardless of what you want to do with it. This approach also reaches its limits when it comes to giving partial access to databases, as the risks of inference and re-identification increase.
Alternatives exist and constitute very active areas of research. They can be grouped into three categories (not separate):
- Data anonymization techniques (eg “k-anonymization”) aimed at making the original data less sensitive.
- Analysis designed to keep data confidential (eg “Differential Privacy”). We use the original data and we desensitize the calculations on the data.
- Generative synthetic data models
These approaches introduce the concept of confidentiality measurement and therefore a quantity associated with data risk. Given their inherent advantages and disadvantages, what approach(es) should be favored in order to optimize the compromise between the risk of confidentiality and the valuation of data?
The National Bank is currently building a data anonymization platform and it would like to be able to identify high potential approaches and develop a prototype solution, based on the chosen approach, on a set of data and practical cases provided by the Bank.
The National Bank will provide simulated structured credit card transaction data.
Full Professor et director – Department of Decision Sciences, HEC Montreal.
Assistant Professor – Computer Science Department, Université du Québec à Montréal.
Leader, Artificial Intelligence,
Information Technology, National Bank of Canada.
Partnership Advisor, IVADO.
NLP Researcher, Croesus.
UG in Computer Engineering, Université de Sherbrooke.
UG in Computer Science, University of Alberta.
Elnaz Karimian Sichani
PhD. in Mathematics & Statistics, University of Ottawa.
Leila Vanessa Nombo
PhD in Mathematics & Statistics, Université Laval.