- The Forensic Data Analyst collects, preserves and analyses complex transactional, operational and digital data to detect fraud, financial crime, regulatory non-compliance and control weaknesses. The role converts digital evidence into clear, reliable and legally defensible intelligence for investigations, management decisions, litigation and regulatory engagement.
- Value to the Group: Timely, accurate and well-governed analytics that strengthen fraud detection, accelerate investigations, quantify exposure, support recovery and improve preventive controls across the Group.
Data Acquisition, Extraction & Preservation
- Acquire data securely from enterprise systems, databases, cloud platforms and email environments in accordance with legal authority, privacy requirements and evidentiary standards.
- Apply appropriate forensic extraction methods, maintain chain of custody, validate completeness and accuracy, and document data-quality limitations.
- Prepare structured and unstructured datasets through indexing, deduplication, normalization and other appropriate processing techniques.
Investigation Analysis & Anomaly Detection
- Translate investigation objectives into analytical questions, data requirements and testable hypotheses.
- Use SQL, Python, R and forensic tools to identify anomalies, hidden patterns, duplicate or irregular transactions, manipulation and behavioral fraud indicators.
- Perform statistical, link, network, log, metadata and audit-trail analysis and develop transaction flows, timelines and event chronologies.
Forensic Continuous Monitoring
- Design, document and maintain the annual fraud-detection and continuous monitoring programme.
- Develop, test, tune and govern rules and use cases, including thresholds, data inputs, exclusions, ownership, review frequency and expected outcomes.
- Triage alerts and exceptions, monitor rule performance, reduce false positives and maintain an auditable repository of scripts, logic, validation evidence and version history.
Reporting, Visualization & Litigation Support
- Translate complex findings into clear reports and non-technical narratives for management, legal counsel, regulators and law enforcement.
- Prepare analytical sections of investigation reports, dashboards, visualizations, exception reports and monthly management statistics.
- Prepare evidence packages and technical documentation and support litigation or regulatory proceedings as required.
Stakeholder Engagement & Continuous Improvement
- Partner with investigators, business owners, IT, Legal and Risk & Compliance on data definitions, remediation actions and delivery timelines.
- Recommend control and process improvements arising from investigations and monitoring.
- Maintain confidentiality, access controls, data-protection compliance and secure evidence handling.
- Perform other duties assigned from time to time.
Knowledge, experience and qualifications required
Knowledge, Experience & Qualifications:
- Bachelor's degree in Mathematics, Statistics, Computer Science, Information Management, Economics, Actuarial Science or another relevant quantitative field.
- Four to six years' relevant experience in data analytics, forensic analytics, fraud monitoring or financial-crime investigation; financial-services, insurance or banking experience is preferred.
- Certification in data analytics or forensic technology tools is required; CFE, CPA(K), CFA, CISSP or a relevant postgraduate qualification is an advantage.
- Demonstrated experience in fraud-rule design, anomaly detection, evidence handling and data-protection requirements.
- Technical / Functional Competencies:
- Advanced SQL and working proficiency in Python or R.
- Forensic analytics, anomaly detection, statistical testing and link analysis.
- Fraud-monitoring rule design, testing, tuning and governance.
- Power BI, Tableau, Qlik Sense or equivalent visualisation tools.
- CaseWare IDEA, Alteryx, Cellebrite or equivalent forensic tools.
- Relational databases, ETL processes and ERP data.
- Digital-evidence preservation, chain of custody, metadata and log analysis.
- Fraud typologies, relevant legislation and evidence-based report writing.