Senior Manager, First Party Fraud Analytics - Lending

Bank of Montreal; Canada; On-site; CAD 105,000 - 215,000; Full time

Benefits offered by this job

Health insurance

Tuition reimbursement

Life insurance

Retirement savings plan

Job summary

BMO is seeking a Senior Manager to lead analytics for First Party Fraud within Enterprise Fraud Management. You will drive data-driven strategies, build risk-based rules, and scale predictive models to improve fraud detection and loss attribution across lending portfolios.

You will collaborate with Technology, Risk, and Business stakeholders, communicate complex insights to executives, and guide analytics best practices while balancing customer experience with business growth in a Canada-wide

Qualifications

• Advanced expertise in fraud analytics, statistical analysis, machine learning, and predictive modeling.
• Strong knowledge of first party fraud risk management and fraud detection methodologies.
• Proficiency with SQL, Python, Power BI, Dataiku, SAS and related analytics tools.

Responsibilities

• Develop risk-based rules to identify FPF behaviors and optimize alerts.
• Generate alerts for lending products and monitor delinquency and score effectiveness.
• Build dashboards with relevant metrics and communicate findings to executives.

Skills

Fraud analytics

Statistical analysis

Machine learning

Predictive modeling

Education

Bachelor’s Degree in a relevant field

Tools

SQL

Python

Power BI

Dataiku

SAS

Job description

Application Deadline: 11/15/2026 Address: 100 King Street West Job Family Group: Data Analytics & Reporting 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.

Key Responsibilities:

• Develop risk‑based rules to identify FPF behaviors, including misrepresentation, synthetic identities, bust‑outs, stolen IDs onboarding and post‑origination fraud.
• Optimize risk‑based rules, triggers, and alerting capabilities to identify first party fraud behaviors.
• Generate alerts for lending products 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.

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 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.

Leadership & Business Skills 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.

Education & Experience

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 products within a fraud scope and various risk engines (ERE, TSYS, MDIF, NCCS) Hands on skills with SQL, Power BI, 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.

Salary:

Salary: $105,000.00 - $215,000.00 Pay Type: Salaried The above represents BMO Financial Group's pay range and type. Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part‑time roles will be pro‑rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group's expected target for the first year in this position. BMO Financial Group's total compensation package will vary based on the pay type of the position and may include performance‑based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards

About Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world. As a member of the