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Forensic Data Analyst at Britam
Britam
October 19, 2026
Full-time
On-site
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.
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.