AIML-FD: AI And Machine Learning For Fraud And Anomaly Detection

AI and Machine Learning for Fraud and Anomaly Detection is a 1-day instructor-led course designed for professionals seeking to combat modern fraud using AI-powered solutions. With digital transactions increasing in volume and complexity, traditional detection methods fall short—AI and machine learning (ML) offer scalable, intelligent alternatives that identify fraud in real time.

This course covers the full pipeline of fraud detection using ML, from understanding fraud types and data structures to developing and evaluating predictive models. You’ll explore supervised, unsupervised, and semi-supervised learning models and apply them to detect both known and unknown fraud patterns.

Participants will:

• Understand the role of AI and ML in fraud prevention across industries like banking, e-commerce, and insurance

• Learn how to preprocess imbalanced datasets—a common challenge in fraud detection

• Gain practical skills in feature engineering, clustering, and model evaluation

• Explore real-world case studies and emerging trends, including deep learning approaches

• Discuss ethical implications and privacy laws related to deploying AI in fraud detection

Hands-on coding exercises using Python and Scikit-learn will guide participants through building a fraud detection model from scratch, covering:

• Model training and testing

• Metrics like precision, recall, ROC-AUC

• Enhancing performance with ensemble methods

By the end of the session, participants will be able to:

• Build and evaluate fraud detection models

• Select the right ML approach for different fraud scenarios

• Apply AI responsibly, balancing detection accuracy with privacy concerns

This course is ideal for data analysts, fraud investigators, IT professionals, and business leaders looking to apply cutting-edge technology to one of the most pressing challenges in risk management.