In today’s fast-evolving financial landscape, accurate credit card spending forecasts are essential for banks to enhance customer service, personalize marketing strategies, and manage credit risk. This comprehensive course, Forecasting Credit Card Spending in Banking, is designed to provide you with the data science skills necessary to drive these crucial insights in a banking environment.

In this course, you’ll develop a robust data model for forecasting credit card spending, learning the essential techniques that power intelligent financial predictions. We start by breaking down the business objectives behind spending forecasts—personalizing credit card offers, identifying high-value customers, detecting fraud, and improving credit risk management. You’ll gain hands-on experience with transaction data, customer demographics, and account information, building a model that enables you to predict spending behavior accurately.

The course explores a range of advanced forecasting models, including LSTM (Long Short-Term Memory), XGBoost, and Artificial Neural Networks (ANN), providing a solid foundation in each technique. You’ll also integrate data sources using Apache NiFi and manage the data in a MySQL data warehouse, simulating real-world banking data flows. By the end of the course, you’ll have the expertise to harness predictive analytics, helping banks enhance customer loyalty, boost operational efficiency, and navigate financial challenges.

Whether you’re a data scientist, financial analyst, or banking professional, this course offers valuable insights and practical skills to elevate your career in finance and analytics

Who this course is for:

• Data Science & Analytics Enthusiasts: Looking to expand skills in banking analytics and predictive modeling.
• Finance Professionals: Interested in leveraging data insights for better customer management and credit risk assessment.
• Banking Specialists: Aiming to enhance customer service, optimize marketing campaigns, and detect anomalies.
• Machine Learning Practitioners: Those looking to apply their ML skills specifically in financial services for impactful results.