Fraud Data Analyst & Client Advisory

CamWebDir; City Of London; On-site; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Annual bonus

Country-specific benefits

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 blend data analytics, fraud investigation, and client engagement to deliver actionable recommendations that improve prevention while reducing friction for legitimate customers.

The ideal candidate has a strong background in fraud analytics, advanced analytics with Python/R, and experience in financial

Qualifications

• Bachelor's degree in a quantitative, technical, analytical, or related field.
• 3+ years of experience supporting 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 proficiency with SQL and Excel for data analysis and reporting.
• Experience investigating fraud, financial crimes, or suspicious activity.

Responsibilities

• Review suspicious activity and fraud alerts to identify risks and trends.
• Analyze large datasets to build and maintain fraud detection rules and models.
• Conduct root cause analyses and develop effective solutions.
• Deliver analyses and recommendations to technical and non-technical stakeholders.
• Advise clients on ThreatMetrix Digital Identity Network and related products.
• Collaborate with fraud managers, risk analysts, developers, and project managers.

Skills

Fraud analytics

Data analysis

Client-facing

Python (pandas)

SQL

Excel

Education

Bachelor's degree in a quantitative/technical field

Tools

Python

R

SQL

Excel

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 blend data analytics, fraud investigation, and client engagement to deliver actionable recommendations that improve prevention while reducing friction for legitimate customers.

The ideal candidate has a strong background in fraud analytics, advanced analytics with Python/R, and experience in financial