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Starts 4 June 2025 21:13

Ends 4 June 2025

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Using AI for Scalable, Accelerated Fraud Detection in Financial Services

Discover how AI can transform fraud detection in financial services by identifying patterns, deploying accelerated computing, and utilizing advanced technologies like graph neural networks to enhance security and build customer trust.
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Overview

Discover how AI can transform fraud detection in financial services by identifying patterns, deploying accelerated computing, and utilizing advanced technologies like graph neural networks to enhance security and build customer trust.

Syllabus

  • Introduction to AI in Financial Services
  • Overview of AI applications in finance
    Importance of fraud detection
    Key AI technologies used for fraud detection
  • Fundamentals of Fraud Detection
  • Types of financial fraud
    Traditional vs. AI-driven methods
    Challenges in current fraud detection techniques
  • Machine Learning for Fraud Detection
  • Supervised learning approaches
    Unsupervised learning and anomaly detection
    Feature engineering for financial data
  • Advanced AI Technologies in Fraud Detection
  • Graph Neural Networks (GNNs)
    Basics of GNNs
    Implementation in fraud detection
    Natural Language Processing (NLP) for fraud analysis
    Deep learning strategies
  • Scalable Systems for Fraud Detection
  • Importance of scalability in financial services
    Accelerated computing technologies (e.g., GPU, TPU)
    Integration with existing financial systems
  • Deploying AI Solutions in Financial Services
  • Building and testing models
    Real-time processing and decision-making
    Case studies of successful AI deployment
  • Security and Ethical Considerations
  • Data privacy and security measures
    Ethical considerations in AI deployment
    Building and maintaining customer trust
  • Future Trends and Opportunities
  • Emerging AI technologies in fraud detection
    The role of AI in future financial services evolution
    Opportunities for innovation and growth
  • Course Wrap-up and Final Thoughts
  • Key takeaways
    Discussion and Q&A
    Additional resources and next steps for learning

Subjects

Computer Science