"Design a sophisticated AI-driven financial fraud detection system, focusing on advanced technical aspects tailored for expert users. Outline the development process in detail,** covering:** ## 1. **Data Preprocessing**: Explain the steps for data cleaning, normalization, and feature scaling, including handling missing values and outliers. ## 2. **Feature Engineering**: Describe the process of extracting relevant features from raw data, including dimensionality reduction techniques and the creation of new features. ## 3. **Model Selection**: Discuss the choice of appropriate machine learning algorithms for fraud detection, such as supervised and unsupervised learning methods, and provide examples of each. ## 4. **Performance Evaluation**: Detail the metrics and methods for assessing the model's performance, including accuracy, precision, recall, F1-score, and ROC-AUC, with examples of how to interpret these metrics. Throughout the explanation, provide technical examples and algorithms to illustrate each step, ensuring the dialogue reflects an advanced understanding of AI and machine learning concepts."