Module 1: Introduction to Fraud Detection

Module 2: Fundamentals of AI and ML in Fraud Detection

Module 3: Data Preprocessing for Fraud Detection

Techniques for handling imbalanced datasets, a common challenge in fraud detection
Feature engineering and selection for improving model accuracy.
Using clustering for anomaly detection in datasets.

Module 4: Machine Learning Models for Fraud Detection

Supervised learning models and their application in known fraud patterns detection
Unsupervised and semi-supervised learning for uncovering unknown fraud patterns.
Ensemble methods to enhance fraud detection accuracy.

Module 5: Implementing ML Models with Python

Walkthrough of setting up the Python environment for ML projects
Coding session: Building a basic ML model for fraud detection using scikit-learn.
Evaluating model performance: Accuracy, Precision, Recall, F1 Score, ROC-AUC

Module 6: Advanced Techniques and Emerging Trends

Deep learning in fraud detection: Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs)
Case studies on the use of AI and ML in detecting sophisticated fraud schemes.
Exploring the future of fraud detection with AI and ML: challenges and opportunities

Module 7: Ethical Considerations and Privacy

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