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A leading AI-powered fraud prevention company in Canada is seeking a motivated Fraud & AML Strategy Co-op. This learning-focused role is ideal for students pursuing a degree in fields like Data Science or Finance who are curious about fraud prevention and risk management. The co-op will assist with rule development and data analysis, contributing to reports and dashboards, while gaining hands-on exposure to the latest fraud detection strategies in a collaborative environment.
About DataVisorDataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.
Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!
We are looking for curious, analytical, and motivated BS or MS co-op students who are interested in fraud prevention, AML, risk analytics, and strategy design. This role is ideal for students who enjoy problem-solving, asking “why” and “what if,” and turning data into actionable insights.
As a Fraud & AML Strategy Co-op, you will work closely with experienced fraud strategists, data scientists, and product teams to help design, test, and optimize fraud and AML detection strategies across payments, account onboarding, and money movement use cases.
This is a learning-focused role with hands-on exposure to real-world fraud patterns, analytics tools, and decision workflows used by global enterprises.