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Introduction Key Learning Outcomes Training Methodology Who Should Attend? Outline

Introduction

As digital ecosystems expand, organisations face increasingly complex risk and fraud scenarios that evolve faster than traditional controls can manage. Artificial intelligence now plays a central role in identifying anomalies, predicting threats, and strengthening early warning systems. However, effective use of AI requires more than technical deployment—it demands governance awareness, risk judgment, and strategic oversight.

This Artificial Intelligence (AI) Powered Risk & Fraud Detection Course addresses both the power and limitations of AI, enabling professionals to deploy these tools responsibly while remaining alert to exploitation risks. The course connects AI capabilities with governance, compliance, and security decision-making to ensure sustainable protection and operational confidence.

Key focus areas include:

Strategic use of AI in risk and fraud detection
Understanding AI vulnerabilities and manipulation risks
Aligning AI detection with governance and compliance expectations
Balancing automation with human oversight

Key Learning Outcomes

At the end of this I Risk & Fraud Detection Course, participants will be able to:

Analyse AI-driven risk and fraud detection capabilities within organisational frameworks
Evaluate emerging AI-enabled threats and attack techniques
Strengthen detection systems while managing AI-related vulnerabilities
Integrate AI tools into broader risk, compliance, and security strategies
Improve governance oversight of AI-based monitoring systems
Apply ethical and regulatory considerations to AI-enabled detection initiatives

Training Methodology

This Risk & Fraud Detection Course applies a practical, expert-led learning approach combining structured instruction with scenario-based discussions and applied analysis. Participants engage with real-world examples to explore how AI detection tools operate in complex environments, ensuring learning outcomes translate directly into improved decision-making and risk control practices.

Artificial Intelligence (AI) Powered Risk & Fraud Detection

Who Should Attend?

This training course is ideal for professionals seeking to enhance risk and fraud detection capabilities using AI-driven approaches, including:

Risk Management Leaders
Compliance and Governance Professionals
Fraud Prevention Specialists
Cybersecurity and Security Managers
Internal Audit and Assurance Professionals
Technology Risk and Control Officers

Course Outline

Day 1

Foundations of AI in Risk and Fraud Detection

AI and Machine Learning Fundamentals for Security
- Core concepts and algorithms relevant to risk detection
- Types of AI models in security applications
- Limitations and blind spots of AI systems
- Real-world applications and failure cases

Understanding the Threat Landscape
- Traditional vs AI-powered threats
- Evolution of fraud techniques
- Social engineering and AI
- Threat modelling with AI considerations

Day 2

Implementation and Integration

Building AI Detection Systems
- Data requirements and quality
- Model selection and training
- Integration with existing security infrastructure
- Performance monitoring and metrics

System Vulnerabilities and Protections
- Common attack vectors against AI systems
- Model poisoning and data manipulation
- Defence strategies and best practices

Day 3

Advanced Detection Techniques

Pattern Recognition and Anomaly Detection
- Behavioural analytics
- Network traffic analysis
- Transaction monitoring
- Feature engineering for fraud detection

Emerging Threats and Countermeasures
- Deepfake detection
- AI-powered social engineering
- Cryptocurrency fraud
- Case Studies: Advanced attack scenarios

Day 4

Governance and Compliance

Regulatory Framework
- Global compliance requirements
- AI system auditing
- Documentation and reporting
- Legal considerations and liability

Ethics and Privacy
- Balancing security with privacy
- Bias in AI systems
- Transparency and explainability
- Developing ethical guidelines

Day 5

Future-Proofing and Strategic Planning

Emerging Technologies and Threats
- Quantum computing implications
- Advanced persistent threats
- Future of AI in security
- Preparing for new attack vectors

Strategic Implementation Workshop
- Risk assessment framework development
- Resource allocation planning
- Training and awareness programs
- Creating a security roadmap