Fraud Detection Techniques in Financial Transactions

Design an AI-driven system to detect and prevent fraudulent activities in real-time financial transactions. Include the following elements:

1. **Key Objectives:** Define the main goals of your fraud detection system, such as accuracy, speed, and scalability.
   
2. **Data Sources:** Identify and describe the types of data your system will analyze (e.g., transaction history, user behavior, location).

3. **AI Techniques:** Outline the machine learning algorithms and techniques you will use (e.g., anomaly detection, neural networks).

4. **Real-Time Processing:** Explain how the system will handle real-time data and alert mechanisms for potential fraud.

5. **Evaluation Metrics:** List the metrics you will use to measure the system's performance (e.g., false positive rate, detection time).

6. **Implementation Challenges:** Discuss potential obstacles and propose solutions, such as data privacy concerns and adaptability to new fraud patterns.

7. **Success Criteria:** Define what a successful implementation would look like, including benchmarks and timelines. 

Craft a detailed plan following these guidelines, ensuring effectiveness and innovation in financial transaction fraud detection.