Director, First Party Fraud Analytics

Hybrid work model: 3 days per week in the office.

We are seeking a strategic and analytical leader to advance BMO’s First Party Fraud (FPF) analytics capabilities. The Director will develop and execute data‑driven strategies to proactively identify, measure and mitigate first-party fraud risk while balancing customer experience, portfolio performance and business growth objectives. Working closely with Technology, Data & Analytics, Risk and Business stakeholders, the Director will leverage advanced analytics, machine learning and behavioral insights to strengthen fraud detection, improve loss attribution and support real‑time risk decisioning.

As Enterprise Fraud Management (EFM) evolves its typology‑based approach to first‑party fraud, the Director will build and enhance a framework that distinguishes fraud from traditional credit risk, enables more effective controls, stronger detection capabilities and improved fraud loss management. Operating at an enterprise level, the Director will influence fraud analytical strategies, drive innovation and serve as a trusted advisor on emerging fraud trends and analytics best practices.

Key Responsibilities

Develop risk‑based rules to identify FPF behaviors including misrepresentation, synthetic identities, bust‑outs, stolen IDs and post‑origination fraud.

Design, implement and continuously optimize risk‑based rules, triggers and alerting capabilities to identify first party fraud behaviors.

Develop early warning indicators and monitoring tools to detect emerging fraud patterns and portfolio shifts.

Evaluate rule effectiveness through performance metrics, loss analysis and ongoing testing.

Ensure appropriate fraud detection frameworks are in place, including application risk assessment, document fraud detection, transaction monitoring and behavioral analytics.

Perform link analysis and data mining to hunt for fraudulent accounts and fraud rings through referrals from Operations/LOB.

Provide strategic oversight to analytics teams to ensure models, rules, triggers and alerts are risk‑based, explainable and effective.

Partner with Fraud Strategy, Investigations and Operations teams to ensure alerting frameworks drive actionable and prioritized investigations.

Use advanced analytics, data mining and behavioral insights to identify new first‑party fraud typologies and translate insights into actionable rules.

Apply advanced analytics, machine learning, statistical modeling and data mining techniques to uncover new fraud risks and behavioral patterns.

Design and scale predictive models and analytical solutions that improve fraud detection and support smarter business decisions.

Conduct large‑scale analysis across diverse data sources to identify trends, opportunities and emerging threats.

Leverage big data tools and modern analytics platforms to enhance fraud monitoring and decisioning capabilities.

Collaborate with product, risk, technology and business teams to support strategic decision‑making, business planning and future roadmap development.

Communicate complex analytical concepts and findings to executive and non‑technical audiences in a clear and compelling manner.

Influence enterprise fraud management strategies through thought leadership, innovation and data‑driven recommendations.

Build strong partnerships across the organization to drive alignment and achieve business objectives.

Foster a high‑performance culture aligned to BMO’s Purpose, Values and strategic priorities.

Attract, develop and retain top analytical talent.

Provide coaching, mentorship and career development opportunities for team members.

Drive accountability, recognize strong performance and support continuous improvement.

Champion diversity, equity and inclusion through leadership actions and team practices.

Qualifications
Technical & Analytical Expertise

Advanced expertise in fraud analytics, statistical analysis, machine learning and predictive modeling.

Strong knowledge of first‑party fraud risk management and fraud detection methodologies related to lending products.

Expert‑level understanding of:

Mathematics, statistics and operations research

Machine learning and deep learning techniques

Data…