Senior Consultant - Fraud Analytics

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The opportunity

We currently have career opportunities for Senior Consultants within our Canadian Fraud Risk Consulting practice with a focus on fraud analytics, detection and data science. Our team helps financial institutions transform fraud risk management through the application of data, artificial intelligence, machine learning, and advanced analytical techniques in an increasingly complex threat landscape.

Emerging technologies, evolving fraud typologies, and real-time payment ecosystems require organizations to enhance their fraud detection capabilities with sophisticated analytical solutions. We help financial institutions strengthen fraud prevention and detection using data analysis, statistical methods, machine learning, and practical improvements to fraud processes and technology.

This job posting relates to an existing vacancy within our organization.

Your key responsibilities

As a Senior Consultant, you will work directly with clients and multidisciplinary EY teams to deliver advanced analytics, machine learning, and data-driven fraud risk solutions. Responsibilities include:

• Design, develop, validate, and deploy fraud detection rules and models across banking and payment products.

• Analyze large and complex datasets to identify emerging fraud patterns, trends, vulnerabilities, and opportunities for enhanced fraud mitigation.

• Collaborate with fraud SMEs, business stakeholders, and technology teams to translate business challenges into analytical use cases and model requirements.

• Support the development of fraud risk strategies across payments, account takeover, identity fraud, synthetic identity, money movement, and emerging fraud typologies.

• Develop fraud dashboards and management reporting to track fraud trends, losses, detection performance, operational outcomes, and key risk indicators.

• Contribute to data preparation, feature engineering, and model monitoring activities that support sustainable fraud analytics programs.

• Present analytical findings and recommendations to senior client stakeholders in a clear, business-oriented manner.

• Contribute to business development activities, proposals, thought leadership, and innovation initiatives focused on fraud analytics, AI, and emerging technologies.

• Evaluate fraud rules or models using measures such as fraud capture, false-positive rates, precision, recall, operational impact, and financial outcomes.

Skills and attributes for success

Advanced Analytical Mindset

• Strong quantitative, statistical, and problem-solving capabilities.

• Ability to transform large volumes of structured and unstructured data into actionable insights.

• Passion for leveraging AI, machine learning, and advanced analytics to solve complex fraud challenges.

High-Performance Teaming

• Demonstrated ability to collaborate across data science, engineering, fraud operations, and business teams.

• Experience managing workstreams and contributing to project delivery within consulting or financial services environments.

Strong Communication Skills

• Ability to communicate technical concepts, model outcomes, and analytical findings to both technical and non-technical audiences.

• Experience creating executive-ready presentations, reports, and client deliverables.

Curiosity and Innovation

• Strong interest in emerging fraud trends, AI technologies, and analytical methodologies.

• Proactive approach to experimentation, innovation, and continuous learning.

Integrity and Inclusiveness

• Commitment to ethical AI principles, model governance, and responsible use of data.

• Ability to thrive in diverse teams and collaborative environments.

To qualify for the role you must have

• At least three years of experience in fraud analytics, data science, machine learning, quantitative risk management, or advanced analytics within financial services or consulting.

• Experience working with banks, credit unions, payment organizations, fintechs, or fraud risk teams.

• Experience designing, testing, tuning or evaluating fraud-detection scenarios, thresholds or risk indicators.

• Strong understanding of fraud detection methodologies, fraud operations, transaction monitoring, or financial crime analytics.

• Experience manipulating and analyzing large-scale datasets and developing practical analytical solutions.

• Proficiency in SQL and experience with at least one analytical programming language or platform, such as Python, R, or SAS.

• Experience with PowerBI.

Ideally, you'll also have

• Bilingual (English/French) considered an asset.

• Hands-on experience developing and validating predictive models using Python, R, SQL, SAS, or similar analytical tools.

• Experience with payment fraud, card fraud, account takeover, digital banking fraud, or first-party fraud.

• Knowledge of MLOps, model governance, explainable AI, and model monitoring practices.

• Exposure to graph analytics, network analytics, entity resolution, or behavioural analytics.

• Exposure to practical uses of generative AI or AI-assisted analytics in fraud risk management is an asset.

• Experience applying supervised and unsupervised machine learning techniques—including gradient boosting, random forests, anomaly detection, clustering, and neural networks—to fraud detection problems is considered an asset.

What we offer

At EY, our Total Rewards package supports our commitment to creating a leading people culture built on high-performance teaming, where everyone can achieve their potential and contribute to building a better working world f