Senior Manager, First Party Fraud Analytics

BMO•Toronto, ON

9d•CA$103,200 - CA$192,000•Hybrid

About The Position

We are seeking a senior analytical leader to advance BMO’s First Party Fraud (FPF) analytics capabilities. This role is accountable for executing data-driven strategies that 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 Senior Manager 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) continues to evolve its typology-based approach to First Party Fraud, this leader will play a critical role in building and enhancing a framework that distinguishes fraud from traditional credit risk, enabling more effective controls, stronger detection capabilities, and improved fraud loss management. Operating at an enterprise level, the Senior Manager will influence fraud analytical strategies, drive innovation, and serve as a trusted advisor on emerging fraud trends, analytics best practices, and the application of data-driven insights to protect the bank and its customers.

Requirements

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 wrangling, preprocessing, and feature engineering
Big data environments and analytical platforms
Data visualization and storytelling
Model governance, ethics, bias, and responsible AI
Experience developing fraud rules, alerts, and risk monitoring frameworks.
Proficiency with data and analytics tools such as SQL, Python, PowerBI, Dataiku, or comparable platforms.
Exceptional analytical, problem-solving, and critical-thinking capabilities.
Strong verbal and written communication skills with the ability to influence senior stakeholders.
Proven ability to lead through ambiguity and drive enterprise-wide outcomes.
Demonstrated success building partnerships across multiple functions and levels of the organization.
Strong collaboration, relationship management, and stakeholder engagement skills.
Experience leading and developing high-performing analytics teams.
Bachelor’s Degree in a relevant field
Typically 7–10+ years of relevant experience in fraud analytics, risk analytics, data science, or a related field, or an equivalent combination of education and experience.
Prior analytical banking experience is an asset.
Experience with lending products fraud, risk engines (ERE, TSYS, MDIF)
Hands on skills with SQL queries, PowerBI, Python, Dataiku, SAS
Prior experience with score vendors (TransUnion, Equifax)
Experience generating alerts for lending products (Credit Cards, Lines of Credit) from Banking Industry
Technical leader and thought partner with demonstrated success influencing strategy and driving transformational change.

Responsibilities

Develop risk‑based rules to identify FPF behaviors, including misrepresentation, synthetic identities, bust‑outs, stolen IDs and post‑origination fraud.
Optimize risk-based rules, triggers, and alerting capabilities to identify first party fraud behaviors.
Generate alerts for lending products (Credit Cards, Lines of Credit) to track delinquency, account monitoring
Assess score effectiveness via Proof of Concept to ingest data into models
Work with score vendor (TransUnion, Equifax) and risk engines
Develop early warning indicators and monitoring tools to detect emerging fraud patterns and portfolio shifts.
Build dashboards with relevant metrics
Evaluate rule effectiveness through performance metrics, loss analysis, and ongoing testing.
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.