AI-Driven Fraud Detection in Banking

Course Info

Length: 1 Week

Type: In Classroom

Available Dates

Venue

  • Dec-30-2024

    Paris

  • Dec-30-2024

    London

  • Dec-30-2024

    Amsterdam

  • Jan-20-2025

    Amsterdam

  • Jan-20-2025

    Barcelona

  • Jan-20-2025

    Paris

  • Jan-20-2025

    Istanbul

  • Jan-20-2025

    Kuala Lumpur

  • Jan-20-2025

    Singapore

  • Feb-10-2025

    Dubai

  • Feb-10-2025

    London

  • Apr-14-2025

    Dubai

  • Apr-14-2025

    London

  • Apr-21-2025

    Amsterdam

  • Apr-21-2025

    Barcelona

  • Apr-21-2025

    Paris

  • Apr-21-2025

    Istanbul

  • Apr-21-2025

    Kuala Lumpur

  • Apr-21-2025

    Singapore

  • June-09-2025

    London

  • June-09-2025

    Dubai

  • July-21-2025

    Istanbul

  • July-21-2025

    Barcelona

  • July-21-2025

    Singapore

  • July-21-2025

    Kuala Lumpur

  • July-21-2025

    Amsterdam

  • July-21-2025

    Paris

  • Aug-11-2025

    London

  • Aug-11-2025

    Dubai

  • Sep-22-2025

    Dubai

  • Sep-22-2025

    London

  • Oct-20-2025

    Barcelona

  • Oct-20-2025

    Singapore

  • Oct-20-2025

    Paris

  • Oct-20-2025

    Amsterdam

  • Oct-20-2025

    Istanbul

  • Oct-20-2025

    Kuala Lumpur

  • Nov-17-2025

    Dubai

  • Nov-17-2025

    London

Course Details

Course Outline

5 days course

Introduction to AI in Fraud Detection


  • Overview of Fraud in the Financial Sector: Understanding the scope and impact of fraudulent activities in banking.
  • AI and Machine Learning Fundamentals: Introduction to the technologies driving modern fraud detection systems.
  • Role of AI in Identifying Fraud Patterns: How AI is used to enhance detection capabilities and speed in identifying fraud.
  • Tools and Technologies: Review of software and tools commonly used in AI-driven fraud detection.
  • Case Studies: Real-world examples of AI successfully preventing fraud in major financial institutions.

Data Handling & Analysis


  • Python for Financial Fraud Detection: Basics of Python programming focused on applications in fraud detection.
  • Exploratory Data Analysis (EDA): Techniques using Python to visualize and analyze transactional data for suspicious patterns.
  • Data Management Best Practices: Ensuring data integrity, security, and privacy when handling financial data.
  • Workshop: Hands-on session where participants use Python to perform EDA on anonymized banking datasets.
  • Discussion: Overcoming challenges in data analysis for fraud detection.

Machine learning concepts & fraud detection



  • Introduction to Machine Learning Models: Overview of supervised and unsupervised learning models used in fraud detection.
  • Unsupervised Learning Techniques: A deep dive into clustering and anomaly detection methods specifically used for identifying unusual patterns indicative of fraud.
  • Hands-On Workshop: Building and applying unsupervised learning models to detect fraud in financial datasets.
  • Model Evaluation and Validation: Techniques to assess the effectiveness of fraud detection models.
  • Q&A session: discussing practical challenges and solutions in machine learning for fraud detection.

Advanced Fraud Detection Using Generative AI


  • Generative AI in Fraud Detection: Introduction to generative models and their applications in understanding complex fraud mechanisms.
  • Analyzing Network and Log Data: Using AI to monitor and analyze vast amounts of network data for signs of fraudulent activity.
  • Hands-On Exercise: Participants develop models to detect fraud using simulated network traffic and transaction logs.
  • Case Study Discussion: Detailed review of a generative AI application in a high-profile fraud detection case.

Deployment & Compliance


  • Deployment Strategies: Best practices for deploying AI systems in a secure and scalable manner.
  • Maintaining Compliance with Financial Regulations: Ensuring that AI fraud detection systems comply with global financial regulations.
  • Evaluating AI Systems Post-Deployment: Tools and Techniques for Monitoring the Performance of AI Systems in Live Environments.
  • Final Project Presentations: Participants present their projects, discussing implementation, challenges, and outcomes.
  • Course Recap and Feedback: Summarizing key concepts, gathering participant feedback, and discussing the next steps for applying the learned skills.