Fraud & Credit Risk Analyst — Payments

Zaver; Stockholms kommun; On-site; SEK 600,000 - 800,000; Full time

Job summary

Zaver, a Swedish fintech founded in 2016 and expanding in Sweden, is seeking an analytical professional to join our fraud prevention team. You will examine transactions, review high-risk cases, monitor patterns, and conduct root-cause analyses of unpaid invoices while contributing to AML investigations and reporting.

Ideal candidates have 3+ years in a similar role, a BSc in Business/Finance/Statistics, and solid SQL and Python skills, with a proactive mindset to enhance fraud-detection

Qualifications

• 3+ years of experience in a similar role.
• Bachelor's degree in Business, Finance, Statistics, or a related field.
• Proficiency in SQL and basic knowledge of Python.
• Ability to thrive in a fast-paced environment.

Responsibilities

• Examine financial transactions and credit histories on a case-by-case basis.
• Review high-risk transactions flagged by internal controls.
• Monitor transaction patterns, identify trends, and flag suspicious activity.
• Conduct root cause analyses of unpaid invoices.
• Handle AML-related investigations and reporting.
• Provide ongoing feedback to management on fraud trends and the effectiveness of fraud detection systems.
• Support the development and improvement of fraud detection processes and routines in connection with broader business initiatives, such as market expansions and new product launches.

Skills

Data analysis

Fraud prevention

Regulatory knowledge

Analytical mindset

Education

BSc in Business/Finance/Statistics

Tools

SQL

Python

Job description

Zaver, a Swedish fintech founded in 2016 and expanding in Sweden, is seeking an analytical professional to join our fraud prevention team. You will examine transactions, review high-risk cases, monitor patterns, and conduct root-cause analyses of unpaid invoices while contributing to AML investigations and reporting.

Ideal candidates have 3+ years in a similar role, a BSc in Business/Finance/Statistics, and solid SQL and Python skills, with a proactive mindset to enhance fraud-detection