Fraud Detection Using AI in Finance

To equip PhD scholars and academicians with advanced skills in utilizing AI and machine learning to detect and prevent fraud in financial services. This course covers anomaly detection, fraud prevention, and the use of AI models to identify suspicious activities in real-time.

Day 1: Introduction to AI in Financial Fraud Detection 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 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 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.

• Develop AI-driven systems for real-time fraud detection.
• Identify anomalies and suspicious activities in financial transactions.
• Build and apply machine learning models for fraud prevention.
• Implement risk management strategies using AI tools.
• Gain hands-on experience in financial fraud detection projects.

Finance professionals, data scientists, risk analysts, cybersecurity experts, and academic researchers.