Associate Analyst - Fraud Risk Management

This position is for fresh graduates UAE Nationals only. The role purpose is to support Fraud Risk Management in improving fraud detection, prevention, sampling, verification monitoring, and control effectiveness through data analytics, automation, and responsible use of AI and machine learning. The graduate role combines fraud-risk development with hands-on analytical, sampling, management information systems (MIS), and process-improvement support.

Responsibilities

• Prepare, reconcile, and quality-check approved fraud datasets while maintaining confidentiality and access controls.
• Analyze fraud model and rule performance, including alert volumes, detection coverage, confirmed outcomes, false positives, false negatives, precision, recall, and efficiency.
• Support analysis of fraud typologies, products, channels, customer segments, payment types, and emerging patterns.
• Assist with controlled testing and impact assessment of fraud rules, thresholds, and analytical models.
• Support AI and machine learning use cases for detection, prioritization, anomaly identification, and investigation assistance.
• Automate repetitive data preparation, reconciliation, alert-triage support, dashboard preparation, and evidence collection using approved tools.
• Maintain dashboards, key risk indicators (KRIs), and key performance indicators (KPIs) for fraud trends, model effectiveness, and operational efficiency.
• Capture investigation feedback and outcomes to support monitoring and future tuning.
• Support validation requests, user acceptance testing (UAT) evidence, change records, issue tracking, and remediation.
• Escalate unusual patterns, data concerns, unexpected model behavior, and potential customer impact.

Requirements

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Business Analytics, Finance, Economics, or a related discipline.
• Fresh graduate or up to two years of relevant internship or work experience.
• Foundation knowledge of statistics, data analysis, and machine learning. Classification metrics or anomaly detection exposure is advantageous.
• Good knowledge of Excel and PowerPoint. Exposure to Python, SQL, R, SAS, Power BI, or automation tools is advantageous.
• Strong analytical curiosity, pattern recognition, documentation, and attention to detail.
• Ability to handle confidential information and follow data, access, and change-control requirements.

Early Career Development Expectations

• Complete required induction, risk, conduct, data protection, and role-specific learning within agreed timelines.
• Develop from supervised task execution to reliable preparation of analyses, working papers, trackers, and management materials.
• Seek feedback, demonstrate continuous learning, and build practical knowledge of the company products, processes, and risk governance.

Indicative First-Year Success Measures

• Accurate and timely completion of assigned analysis, documentation, reporting, and follow-up activities.
• Complete, traceable, and well-organized records with minimal rework required.
• Constructive engagement with stakeholders and consistent adherence to approved governance and control requirements.
• Effective support to fraud sampling cycles, including accurate sample lists, evidence packs, exception trackers, and concise MIS highlighting key themes and required follow-up.