Business Insights Specialist, Insider Fraud Detection & Analytics

Work Location:

Markham, Ontario, Canada

Hours:

37.5

Line of Business:
Analytics, Insights, & Artificial Intelligence

Pay Details: $96,900 - $136,800 CAD

This role is eligible for a discretionary variable compensation award that considers business and individual performance.

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience  compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Summary

The Business Insights Specialist, Insider Fraud Detection & Analytics, is responsible for supporting the development and enhancement of insider fraud detection strategies through advanced analytics, business intelligence, and fraud risk identification. This role partners with Fraud, Investigations, Compliance, Risk, and Technology teams to identify emerging insider threats, monitor employee activity for unusual patterns, and drive data-informed decision-making.

The successful candidate will leverage fraud expertise, strong analytical thinking, and advanced coding skills in Python and SQL to develop actionable insights, support detection rule development, and enhance the effectiveness of insider fraud monitoring programs.

While direct Insider Fraud experience is preferred, candidates with experience in First-Party Fraud, Third-Party Fraud, Financial Crimes, AML, Investigations, or Risk Analytics are strongly encouraged to apply.

• Analyze large, complex datasets to identify suspicious employee activity, fraud trends, and emerging insider risks.
• Support the development and optimization of fraud detection scenarios through data-driven analysis.
• Conduct trend, behavioral, and risk-based analyses to identify potential indicators of insider misconduct.
• Perform root cause analysis on fraud events and detection gaps.
• Recommend enhancements to existing monitoring and detection strategies.

• Develop and maintain dashboards, scorecards, and reporting solutions that measure fraud risk exposure and detection effectiveness.
• Translate complex analytical findings into meaningful business insights for senior leadership and operational partners.
• Produce recurring and ad hoc reporting on fraud trends, detection performance, and key risk indicators.
• Monitor and evaluate detection rule performance, false positive rates, and alert volumes.

• Develop advanced SQL queries to extract, transform, and analyze data from multiple enterprise data sources.
• Utilize Python to automate analytics processes, perform statistical analyses, and develop scalable monitoring solutions.
• Support data validation, quality assurance, and analytical model development.
• Create repeatable processes and automated reporting solutions to improve operational efficiency.

• Collaborate closely with Fraud Strategy, Investigations, Compliance, Risk Management, Technology, and Data Engineering teams.
• Gather business requirements and translate them into analytical solutions.
• Present findings, recommendations, and key insights to stakeholders at various levels of the organization.
• Support strategic fraud initiatives and ongoing enhancement activities.

• 3+ years of experience in Fraud Management, Fraud Analytics, Financial Crimes, AML, Investigations, Risk Analytics, or a related field.
• Experience identifying fraud trends and developing data-driven recommendations.
• Strong understanding of fraud typologies, fraud controls, and investigative processes.

• Complex joins and subqueries
• Analytical and window functions
• Data reconciliation and validation
• Large-scale data analysis

• pandas
• numpy
• data transformation and automation
• statistical analysis
• anomaly detection techniques
• reporting automation

• Strong problem-solving and critical thinking skills.
• Ability to identify trends, anomalies, and emerging…