Data Analyst - Fraud & Financial Crime

About the role

N26 is a digital bank headquartered in Berlin that serves millions of customers across Europe with mobile-first banking services. The Data Analyst

Fraud & Financial Crime position focuses on identifying and preventing fraudulent transactions and financial crime activities within the platform. This role works closely with the compliance and risk teams to ensure the bank meets all regulatory obligations and protects customers from financial harm. You will analyze large volumes of transactional data to uncover suspicious patterns and support the development of detection strategies that reduce risk exposure. Your work will directly contribute to safeguarding the integrity of the banking platform and maintaining trust with customers. You will also contribute to the continuous improvement of fraud detection frameworks by sharing insights from your analyses with the broader risk community. The analyst will report to the head of the fraud analytics team and collaborate with data engineers to ensure data quality and availability.

Key facts

What you'll do

• Examine transactional data to identify anomalies that may indicate fraudulent activity or financial crime.
• Build and maintain dashboards that track fraud indicators and support decision-making for the risk team.
• Collaborate with the fraud investigation unit to provide data-driven insights on suspicious account behavior.
• Develop statistical models and rule-based systems to improve the accuracy of fraud detection over time.
• Produce regular reports on fraud trends, financial crime patterns, and detection performance metrics for leadership.
• Work with the compliance team to ensure all analytical outputs align with regulatory requirements and standards.
• Support the enhancement of existing fraud scoring models by validating and testing new analytical features.
• Conduct ad hoc analyses to investigate specific cases or emerging threats in financial crime activity.
• Partner with engineering teams to integrate fraud detection logic into the core banking platform systems.
• Document methodologies, assumptions, and findings to ensure reproducibility and transparency in all analytical work.
• Monitor transaction flows in real time to flag potential breaches and escalate alerts to the appropriate investigation team.
• Coordinate with external partners and law enforcement agencies when financial crime investigations require data support or expert analysis.
• Evaluate the effectiveness of existing fraud detection rules by analyzing false positive and false negative rates across different transaction categories.
• Provide training and guidance to junior team members on analytical methods and best practices for fraud data analysis.

Requirements

• Bachelor's degree in a quantitative field such as statistics, mathematics, economics, or computer science is required for this position.
• Strong analytical skills with the ability to interpret complex datasets and draw actionable conclusions from raw data.
• Proficiency in SQL for querying and manipulating large-scale relational databases with confidence, accuracy, and efficiency is expected.
• Experience with data visualization tools to communicate findings to both technical and non-technical stakeholders effectively.
• Understanding of fraud typologies and financial crime regulations relevant to the banking and financial services sector.
• Ability to work independently and manage multiple analysis projects with competing priorities and tight deadlines.
• Excellent written and verbal communication skills for presenting analytical results to cross-functional teams in a clear manner.
• Attention to detail and a methodical approach to problem-solving in high-pressure, fast-paced working environments with minimal supervision.
• Familiarity with regulatory frameworks and compliance standards in the financial services industry is a strong advantage.

Nice to have

• Familiarity with Python or R for statistical modeling and data manipulation tasks in a professional setting.
• Prior experience working in a regulated industry such as banking, fintech, or insurance is highly valued.
• Knowledge of machine learning techniques applied to anomaly detection and pattern recognition problems in transaction data.
• Understanding of European financial regulations including Anti-Money Laundering directives and Know Your Customer requirements.

Skills & tools

• SQL for data extraction, transformation, and querying of large-scale relational databases.
• Data visualization platforms for building interactive dashboards and sharing reporting views with stakeholders.
• Statistical analysis methods including hypothesis testing, regression modeling, and clustering techniques for data exploration.
• Spreadsheet tools for quick data validation checks, calculations, and exploratory analysis of structured datasets.
• Version control systems for tracking changes in analysis code, scripts, and supporting documentation files.
• Business intelligence platforms for aggregating fraud-related metrics and presenting insights to leadership teams.
• Programming languages such as Python or R are useful for automating repetitive data tasks and building custom analysis scripts.

Practical notes

• This role is based in the Berlin office and requires on-site presence during standard working hours.
• The team operates in a fast-paced environment where timely delivery of analytical insights is essential.
• Candidates should be prepared to work with sensitive financial data under strict confidentiality and privacy protocols.
• The hiring process may include multiple rounds of interviews, including a technical case study and a behavioral assessment.
• The position offers the chance to work with a diverse team of analysts, engineers, and compliance professionals in a collaborative setting.
• N26 values diversity and inclusion, and candidates from all backgrounds are encouraged to apply for this position.