Claims Data Analyst

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Are you looking for a caring, collaborative, values-driven workplace with inspiring teammates and leaders? Do you have the ambition and desire to be the best and thrive at one of the world’s leading insurance providers? Look no further than Zurich Canada.

Zurich Canada is currently looking for a Claims Fraud Analyst to join the Data, Analytics & Artificial Intelligence team, supporting Claims and Fraud partners.

This role will help strengthen Zurich Canada’s ability to identify potential claims fraud, leakage, unusual patterns, and emerging risk indicators through data, analytics, and business insight.

The successful candidate will work closely with Claims, Special Investigations Unit (SIU), Technology, and analytics teams to review claims data, develop fraud indicators, validate trends, and translate findings into clear recommendations that support better claims outcomes.

This is a unique opportunity to combine claims knowledge, analytical thinking, and investigative curiosity to help protect Zurich, our customers, and the integrity of the claims process. This posting is for an existing vacancy.

Zurich Canada uses artificial intelligence-enabled tools to support certain aspects of the recruitment process, including the initial review and screening of applications. Artificial intelligence is not the sole basis for candidate shortlisting or selection. All hiring decisions are reviewed and made by qualified hiring professionals.

Zurich follows a hybrid work model requiring in-person presence, which may include time in the office or market-facing engagements.

• Analyze claims data to identify potential fraud patterns, unusual activity, leakage, over payments, and other risk indicators.
• Partner with Claims and SIU teams to support investigation prioritization, case reviews, and indicator refinement.
• Review historical fraud cases and pending claims to identify recurring themes, emerging trends, and opportunities to improve detection.
• Develop dashboards, reports, and analysis that help business partners understand fraud trends, payment patterns, vendor activity, and claim behaviours.
• Support the design, testing, and monitoring of fraud indicators, rules, models, and alerts in partnership with data and technology teams.
• Conduct deep-dive analysis on claim payments, payees, vendors, body shops, service providers, recoveries, and claims expenses.
• Validate data quality, definitions, and business rules to help ensure fraud insights are accurate, explainable, and actionable.
• Prepare clear summaries, recommendations, and presentations for Claims, SIU, and leadership stakeholders.
• Collaborate with cross-functional teams to improve data access, reporting consistency, and the responsible use of analytics in fraud detection.
• Stay current on emerging fraud trends, analytical techniques, and opportunities to use data and AI-enabled capabilities responsibly.

Required:

• Bachelors Degree and 3 or more years of experience in the Statistical Analysis area

OR

• High School Diploma or Equivalent and 5 or more years of experience in the Statistical Analysis area

Preferred:

• Bachelor’s Degree and 2+ years of experience in claims, fraud, analytics, business intelligence, investigations, insurance operations, or a related field.
• Strong analytical skills with the ability to interpret data, identify patterns, and connect insights to business actions.
• Experience working with claims data, operational data, payment data, or other complex business datasets.
• Strong communication skills with the ability to explain findings clearly to both technical and non-technical audiences.
• Strong attention to detail, curiosity, sound judgment, and ability to handle confidential information appropriately
• Insurance claims, fraud, special investigation, or claims analytics experience.
• Experience with Power BI, SQL, Databricks, Python, or similar analytics tools.
• Experience analyzing claims payments, vendor patterns, recoveries, leakage, expenses, or fraud indicators.
• Understanding of claims processes, claim systems, policy…