Role: Associate Analyst - Fraud Risk Management

Location: Sharjah, UAE

This position is for fresh graduates UAE Nationals only.

Role purpose:

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, MIS and process-improvement support.
Under supervision, assist fraud operations, model owners, technology, data and model risk teams with data preparation, performance analysis, testing, workflow automation and documentation.

Key accountabilities/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 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, KRIs and KPIs for fraud trends, model effectiveness and operational efficiency.
Capture investigation feedback and outcomes to support monitoring and future tuning.
Support validation requests, UAT evidence, change records, issue tracking and remediation.
Apply explainability, fairness, data quality, privacy, security, human oversight and auditability controls.
Escalate unusual patterns, data concerns, unexpected model behavior and potential customer impact.
Support fraud, risk sampling across products, sourcing channels and customer acquisition processes, including sample extraction, evidence collation, outcome tagging and follow-up of exceptions.
Assist sampling reviews for application fraud, contact point verification, documentary checks, impairment indicators, syndicate-fraud signals and vendor or agency control monitoring.
Prepare sampling MIS and trend analysis to highlight control gaps, recurring exceptions, turnaround-time adherence and areas requiring corrective action by Fraud Risk, Operations, Underwriting or business teams.
Support controlled mystery-shopping, seeding, queue monitoring and alert-review analytics where required, ensuring findings are documented, escalated and linked to control improvement actions.

Education and experience:

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Business Analytics, Finance, Economics or 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.
Working understanding of fraud sampling, application verification, contact point verification, preliminary investigation support, fraud MIS preparation and control-exception tracking are advantageous.

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 ADIB products, processes and risk governance.
Use approved data, systems and AI tools responsibly, maintaining confidentiality, evidence and appropriate human review.
Demonstrate integrity, professional judgement, collaboration and timely escalation of issues or uncertainty.

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.
Visible development of technical knowledge, analytical capability and professional communication.
Practical contributions to at least one controlled efficiency, automation or process-improvement opportunity.