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Clinical Manager, Birthing Services| Permanent Full Time
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ON · ON, Canada
- Full-time
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- Full-time
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Sr. Manager Corporate Development
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Added 12 hours ago
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Senior Manager, First Party Fraud Analytics - Onboarding
BMO
Toronto, ON
- Full-time
Partner with Fraud Strategy, Investigations, and Operations teams to ensure alerting frameworks drive actionable and prioritized investigations.
Added 12 hours ago
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BMO

Senior Manager, First Party Fraud Analytics - Cards

Toronto, ON, Canada
FCP, Canada

$105,000.00 - $215,000.00 · Full-time

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Job details

Pay: $105,000.00 - $215,000.00
Job type: Full-time
Apply by: Nov 16, 2026
Experience: 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.
Posting language: English
Posted: Nov 2, 2026

Original 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 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.

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)
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:

$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 BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.

To find out more visit us at https://jobs.bmo.com/ca/en.

BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other’s differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.

Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.

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More about this job

Responsibilities

Lead enterprise first-party fraud analytics by developing and optimizing risk rules, alerts, predictive models, and monitoring tools for credit cards and lines of credit. Partner with fraud, risk, technology, and business teams to identify emerging fraud patterns, improve loss management, guide investigations, and influence enterprise strategy.

Requirements

Requires a bachelor’s degree in a relevant field and typically 7–10+ years of experience in fraud analytics, risk analytics, data science, or a related area. Candidates should bring advanced fraud and modeling expertise, hands-on skills with SQL, Python, Power BI, Dataiku, or SAS, experience with lending products and risk engines, and strong leadership and stakeholder communication abilities.

Skills

Fraud Analytics
First-Party Fraud Risk Management
Statistical Analysis
Machine Learning
Predictive Modeling
Fraud Detection
Risk-Based Rules
SQL
Python
Power BI
Dataiku
SAS
Data Mining
Feature Engineering
Model Governance
Team Leadership

Benefits

Health Insurance
Tuition Reimbursement
Accident Insurance
Life Insurance
Retirement Savings Plans
Performance-Based Incentives
Discretionary Bonuses

Visa sponsorship

Not detected in the job text

Keywords

First-Party Fraud
Enterprise Fraud Management
Fraud Analytics
Fraud Risk Management
Credit Cards
Lines Of Credit
Risk Engines
Early Warning Indicators
Risk-Based Rules
Alerting Frameworks
Loss Attribution
Machine Learning
Deep Learning
Predictive Modeling
Statistical Analysis
Data Mining
Behavioral Insights
Feature Engineering
Big Data
SQL
Python
Power BI
Dataiku
SAS
TransUnion
Equifax
ERE
TSYS
MDIF
Model Governance

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