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AI-Powered Credit Card Spending Forecasting in Banking
228 students
AI-Powered Credit Card Spending Forecasting in Banking
Master Predictive Analytics for Credit Card Spending in Banking
Created byEduconnhub, Dien Pham
Last updated 4/2026
English
What you'll learn
- Understand Banking Forecasting Needs: Learn the essentials of credit card spending forecasts to boost customer service, personalize offers, and assess credit ri
- Build Predictive Models: Develop advanced forecasting models using LSTM, XGBoost, and ANN to predict spending patterns and identify trends.
- Data Integration Skills: Master Apache NiFi for real-time data integration, crucial for connecting customer, transaction, and account data.
- Data Management with MySQL: Organize and manage data in a robust MySQL data warehouse for seamless analytics.
- Applications in Banking: Gain insights for fraud prevention, customer targeting, and credit risk management.
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Course content
3 sections • 18 lectures • 2h 43m total length
Requirements
- Basic Knowledge of SQL: Familiarity with SQL queries and database operations.
- Machine Learning Basics: Some understanding of machine learning algorithms.
- Interest in Banking Analytics: Suitable for those aiming to apply data analytics in banking or finance.
- Tools Needed: Access to Python, MySQL, and Apache NiFi.
Description
*This course contains the use of artificial intelligence.*
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.