Data-Driven Fraud Detection in Banking

Objectives to cover:

1. Introduction to Data-Driven Fraud Detection
2. Understanding the Scope of Fraud in the Banking Sector
3. The Role of Data Engineering in Financial Security
4. Building Robust Data Pipelines for Fraud Detection
5. Key Data Sources Used in Fraud Detection
6. Data Cleaning and Transformation for Accurate Analysis
7. Real-Time Data Processing and Its Impact on Fraud Prevention
8. Machine Learning and AI in Data-Driven Fraud Detection
9. Challenges in Data Engineering for Fraud Detection
10. The Role of Cloud and Big Data Technologies
11. Case Studies: Successful Data Engineering in Fraud Detection
12. Future Trends in Data Engineering for Financial Security
13. Conclusion: Strengthening Financial Security Through Data Engineering

Reference:  APA style