Workshop Objectives:

• Understand AI’s role in detecting financial fraud.
• Build machine learning models for anomaly detection.
• Apply AI techniques to identify suspicious financial activities.
• Design fraud prevention strategies with real-time detection.
• Gain hands-on experience in building AI-driven fraud detection systems.

What you will learn?

Day 1: Introduction to AI in Financial Fraud Detection
Duration: 1 Hour
Objective: Introduce participants to the basics of AI technologies used for detecting fraud in financial services.
Session Details:

• Understanding Financial Fraud: Types of fraud encountered in financial services including credit card fraud, insurance fraud, and cyber fraud.
• Role of AI in Fraud Detection: Overview of how AI can enhance fraud detection and the types of AI technologies used.
• Introduction to Machine Learning: Basic concepts and algorithms commonly used in fraud detection (e.g., classification, clustering).
• Hands-On Activity: Setting up the machine learning environment and exploring initial financial datasets.

Day 2: Machine Learning Techniques for Fraud Detection
Duration: 1 Hour
Objective: Dive deeper into specific machine learning techniques and their applications in detecting financial fraud.
Session Details:

• Feature Engineering for Fraud Detection: Techniques for selecting and engineering features from transactional data that are indicative of fraudulent activities.
• Anomaly Detection Techniques: Implementing algorithms like isolation forests, neural networks, and unsupervised learning methods to identify unusual patterns.
• Model Training and Evaluation: Training models on historical fraud data and evaluating their performance.
• Hands-On Activity: Building a machine learning model to identify potential fraud in a set of transactional data.

Day 3: Implementing and Optimizing Fraud Detection Models
Duration: 1 Hour
Objective: Learn how to deploy, monitor, and optimize AI models in real-world financial settings.
Session Details:

• Deploying AI Models: Strategies for integrating AI fraud detection models into existing financial systems.
• Model Monitoring and Updating: Techniques for monitoring model performance over time and updating models to adapt to new fraudulent tactics.
• Case Studies: Discussion of real-world applications and success stories in AI-driven fraud detection.
• Hands-On Activity: Simulating the deployment of a fraud detection model and monitoring its alerts on sample financial transactions.