1. Introduction
  - Background and Significance
  - Objectives and Scope
2. Literature Review
  - Overview of Fraud Detection Techniques
  - Machine Learning Algorithms for Fraud Detection
  - Evaluation Metrics for Fraud Detection Systems
3. Data Preprocessing and Feature Engineering
  - Data Collection and Preprocessing
  - Feature Selection and Engineering
  - Data Imbalance Handling
4. Model Development
  - Selection of Machine Learning Algorithms
  - Model Training and Validation
  - Hyperparameter Tuning and Optimization
5. System Integration and Real-Time Processing
  - Integration with Transaction Processing Systems
  - Real-Time Fraud Detection Pipeline
  - Performance Monitoring and Alerting