Fraud Data Analyst — Detect & Prevent Financial Crime

Brightmine; New York (NY); On-site; USD 126,000 - 210,000; Full time

Benefits offered by this job

Annual incentive bonus

Job summary

LexisNexis Risk Solutions is seeking a Fraud Data Analyst to support large banking, financial services, and e-commerce clients in implementing and optimizing fraud and identity solutions. You will analyze fraud patterns, develop detection strategies, and provide actionable recommendations to reduce risk while minimizing friction for legitimate customers.

The role blends data analytics with client engagement, requiring strong Python/SQL skills, experience in financial services, and the ability to

Qualifications

• Bachelor's degree in a quantitative, technical, analytical, or related field.
• 3+ years of experience in customer-facing technology, analytics, or fraud prevention.
• 3+ years in financial services, banking, fintech, payments, risk management, or e-commerce.
• 3+ years of advanced analytics using Python, R, or similar tools.
• Strong SQL and Excel proficiency for data analysis and reporting.

Responsibilities

• Review suspicious activity and complex fraud cases to identify risks and trends.
• Analyze large datasets to build and optimize fraud detection rules, models, and workflows.
• Conduct root cause analyses to investigate client issues and develop solutions.
• Deliver analyses and recommendations to stakeholders via presentations and reports.
• Advise on implementing ThreatMetrix Digital Identity Network and related products.

Skills

SQL

Python

Excel

Fraud Analysis

Communication

Education

Bachelor's degree in a quantitative field

Tools

ThreatMetrix

Job description

LexisNexis Risk Solutions is seeking a Fraud Data Analyst to support large banking, financial services, and e-commerce clients in implementing and optimizing fraud and identity solutions. You will analyze fraud patterns, develop detection strategies, and provide actionable recommendations to reduce risk while minimizing friction for legitimate customers.

The role blends data analytics with client engagement, requiring strong Python/SQL skills, experience in financial services, and the ability to