In today’s competitive financial landscape, personalized credit card recommendations have become essential for banks and financial institutions looking to elevate customer satisfaction and reduce churn. This course, Personalized Credit Card Recommendations in Banking, is designed to equip you with the tools and techniques to design, build, and deploy recommendation systems that align with individual customer needs, spending behaviors, and preferences. We’ll start with understanding the importance of personalizing recommendations and the challenges in product identification. Through hands-on projects, you’ll learn how to use customer profiles, credit history, and spending habits to predict approval probabilities accurately, ensuring that customers receive the right card recommendations.

You’ll also dive into credit limit determination models to balance customer satisfaction with credit risk management, maximizing engagement while minimizing default risks. A special focus will be on aligning reward types—such as cashback, travel, or points—based on customer spending behavior to increase loyalty and engagement. By the end of the course, you’ll have practical experience using Apache NiFi, MySQL, and machine learning techniques, including Random Forest and XGBoost, for a fully automated, data-driven recommendation system. This course is ideal for data professionals, banking personnel, and fintech enthusiasts eager to implement personalized, AI-powered solutions in the banking sector.

Who this course is for:

• Data scientists and analysts eager to work with financial data.
• Banking and finance professionals looking to improve product offerings
• Students or professionals interested in machine learning applications.
• Anyone passionate about fintech and personalized customer experiences.