Advanced Data Analytics for Fraud Detection in Financial Services Training

The programme

Programme overview

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.

2026 has changed the shape of this fight. The same generative and agentic AI that helps fraud teams score transactions in milliseconds and draft investigation summaries is now also being used to manufacture synthetic identities, clone voices, and generate convincing deepfakes at scale. This training bridges the gap between data analytics and fraud investigation by enabling participants to translate analytical findings into actionable investigative intelligence and reporting insights — built for a landscape where AI sits on both sides of the fraud fight.

Who should attend

Who is this programme for?

• Fraud Investigation Teams
• Risk Management Professionals
• Internal Auditors
• Compliance Officers
• Investigators
• Data Analysts

Learning outcomes

What you will learn

• Understand fraud typologies within structured financial datasets, including AI-generated fraud such as synthetic identities and deepfakes
• Apply data analytics techniques, including AI and machine learning models, to detect fraud risks
• Identify anomalies, behavioural patterns, and irregular transactions
• Support investigations with data-driven evidence, including AI-assisted case narratives
• Develop effective fraud monitoring and reporting outputs, applying explainable AI principles for regulatory transparency

Explore the programme

Detailed programme outline

2-Day Essentials — Training Outline

• Evolution of fraud in banking and insurance
• The AI arms race: generative AI powering synthetic identity factories, deepfakes, and voice cloning on the offence, and machine learning scoring, generative case summaries, and agentic monitoring on the defence
• Regulatory expectations and compliance environment
• Transactional anomalies and irregular patterns
• Behavioural profiling of fraud suspects, including detecting synthetic identities and deepfake-based impersonation
• Insider fraud indicators and collusion patterns
• Traditional statistical methods vs. AI-driven anomaly detection
• Risk scoring models: rule-based systems and machine learning models
• Segmentation and peer group comparisons
• Linking data patterns to investigative leads
• Using generative AI to draft case narratives and Suspicious Activity Report (SAR) summaries, then verifying for accuracy
• Documentation standards for evidence trails
• Fraud dashboards and KPI reporting
• Explainable AI: ensuring AI-driven fraud decisions can be justified to regulators and customers
• Ethical use of data analytics, data privacy, and confidentiality obligations
• Deciding whether the 5-Day Comprehensive programme is the right next step

5-Day Comprehensive — Training Outline

• Internal vs external fraud ecosystems
• Emerging fraud trends: the AI arms race — synthetic identity factories, deepfakes, and voice cloning on the offence; machine learning scoring, generative case summaries, and agentic monitoring on the defence
• Role of analytics in modern fraud prevention frameworks
• Banking transactional datasets (cards, EFTs, loans, payments)
• Insurance datasets (claims, underwriting, policies)
• Customer profiling and KYC data
• Third-party and external data sources, including biometric and digital identity data for AI-based verification
• Data quality, governance, and integrity challenges
• Claims frequency, severity, and duplication analysis
• Exception reporting and threshold setting, balancing fraud capture against false positives
• Descriptive and diagnostic analytics
• Trend analysis and time-series review
• Traditional statistical methods vs. AI-driven anomaly detection: supervised and unsupervised machine learning approaches
• Risk scoring models and rule-based systems
• Case selection using analytics outputs
• Using generative AI to draft investigative case narratives and Suspicious Activity Report (SAR) summaries, then verifying for accuracy
• Collaboration between analysts and investigators
• Regulatory reporting requirements
• Explainable AI: ensuring AI-driven fraud decisions can be justified to regulators, auditors, and customers
• Ethical use of data analytics and AI models, including managing model bias
• Data privacy and confidentiality obligations
• Continuous improvement of fraud analytics frameworks

Learning approach

How you will learn

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.

Discuss your requirements

Dates, duration & arrangements

Contact Ratomac to confirm dates, duration, delivery format, entry requirements and any applicable assessment or certification before booking.