Master advanced data analytics for fraud detection. Learn to identify anomalies, profile behavioral risks, and translate complex datasets into actionable intelligence.
| Date | Venue | Duration | |
|---|---|---|---|
| 31 August - 04 September 2026 | Sandton | 5 Days | |
| 12 - 16 October 2026 | Sandton | 5 Days | |
| 09 - 13 November 2026 | Sandton | 5 Days |
This Advanced Data Analytics for Fraud Detection in Financial Services Training course provides a practical and structured approach to leveraging data analytics for fraud detection, prevention, and investigation within banking and insurance environments. Participants are equipped with the skills to interpret complex financial, transactional, and claims data to identify anomalies, behavioural risks, and emerging fraud patterns. The training bridges the gap between data analytics and fraud investigation by enabling participants to translate analytical findings into actionable investigative intelligence and reporting insights.
By the end of the course, participants will be able to:
Understand fraud typologies within structured financial datasets
Apply data analytics techniques to detect fraud risks
Identify anomalies, behavioural patterns, and irregular transactions
Support investigations with data-driven evidence
Develop effective fraud monitoring and reporting outputs
Fraud Investigation Teams
Risk Management Professionals
Internal Auditors
Compliance Officers
Investigators
Data Analysts
Our diverse instructional approaches ensure effective learning:
– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.
Module 1: Fraud Landscape in Financial Services
Evolution of fraud in banking and insurance
Internal vs external fraud ecosystems
Emerging fraud trends (digital fraud, synthetic identities)
Regulatory expectations and compliance environment
Role of analytics in modern fraud prevention frameworks
Module 2: Financial Data Ecosystem and Sources
Banking transactional datasets (cards, EFTs, loans, payments)
Insurance datasets (claims, underwriting, policies)
Customer profiling and KYC data
Third-party and external data sources
Data quality, governance, and integrity challenges
Module 3: Fraud Indicators and Behavioural Red Flags
Transactional anomalies and irregular patterns
Behavioural profiling of fraud suspects
Claims frequency, severity, and duplication analysis
Insider fraud indicators and collusion patterns
Exception reporting and threshold setting
Module 4: Analytical Techniques for Fraud Detection
Descriptive and diagnostic analytics
Trend analysis and time-series review
Outlier and anomaly detection methods
Risk scoring models and rule-based systems
Segmentation and peer group comparisons
Module 5: Data-Driven Fraud Investigations
Case selection using analytics outputs
Linking data patterns to investigative leads
Building investigative narratives from data
Documentation standards for evidence trails
Collaboration between analysts and investigators
Module 6: Governance, Reporting & Ethical Considerations
Fraud dashboards and KPI reporting
Regulatory reporting requirements
Ethical use of data analytics
Data privacy and confidentiality obligations
Continuous improvement of fraud analytics frameworks
Discover how our courses enhance professionals’ effectiveness in their workplaces.
Very happy with the training. It was excellently delivered and comprehensively covered all critical aspects of ESG and effectively contextualised them within the environments of our respective organisations.
The course was eye opening and exceeded my expectations.
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