An Interactive 5-Day Training Course

Artificial Intelligence (AI) Powered Risk & Fraud Detection

Securing Your Organization's Future: Mastering AI for Advanced Threat Detection and Prevention

22 - 26 Dec 2025
London
| $5950
13 - 17 Apr 2026
Amsterdam
| $5950
27 - 31 Jul 2026
Dubai
| $5950
28 Sep - 02 Oct 2026
Paris
| $5950
21 - 25 Dec 2026
London
| $5950

Introduction

This training course equips senior risk, compliance, and security professionals with advanced knowledge of AI-powered detection systems, their limitations, and potential vulnerabilities. In an era where digital threats evolve at unprecedented speeds, artificial intelligence has become a critical tool in risk management and fraud detection.

 Participants will learn to balance the powerful capabilities of AI in detecting threats with an understanding of its potential weaknesses and exploitation points.

 The training course provides a dual perspective: leveraging AI's strengths in identifying patterns and anomalies while remaining vigilant about emerging AI-enabled threats and attack vectors. This training course provides a comprehensive approach to both defensive and offensive considerations in AI security. Participants will learn not only how to implement AI for detection but also understand how adversaries might attempt to manipulate or bypass these systems. 

This GLOMACS Artificial Intelligence (AI) Powered Risk & Fraud Detection training course will highlight:

Key Learning Outcomes

By the end of this training course, participants will be able to:

Training Methodology

The Artificial Intelligence (AI) Powered Risk & Fraud Detection training course utilizes a comprehensive, multi-faceted learning approach to ensure effective knowledge acquisition. It combines expert-led instruction with interactive small-group exercises that encourage collaboration and deeper understanding. Learning is further reinforced through the use of engaging videos that illustrate key concepts and real-world case analysis, allowing participants to apply what they've learned to practical scenarios.

Artificial Intelligence (AI) Powered Risk & Fraud Detection

Who Should Attend?

Organisations implementing the strategies and systems covered in this program can expect to significantly enhance their risk management and fraud detection capabilities. The training course provides frameworks for building robust, adaptable security that leverage AI while maintaining critical human oversight and intervention capabilities.

Key Impact Areas:

  • Enhanced early warning systems for emerging threats and fraud patterns
  • Reduced false positives in fraud detection, leading to operational efficiency
  • Improved regulatory compliance through advanced monitoring capabilities
  • Strengthened defence against AI-powered attacks and social engineering
  • Decreased financial losses from fraud through proactive detection

Learning Journey Breakdown

  • 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
  • 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
  • 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
  • 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
  • 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

Ready to Take the Next Step?

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