Forensic Data Analyst at Britam

Job Purpose:

• 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. 

Key Responsibilities: 

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.