AI-Powered Credit Card Spending Forecasting in Banking

Name: AI-Powered Credit Card Spending Forecasting in Banking
Rating: 5.0 (1 reviews)

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.

Course content

3 sections • 18 lectures • 2h 43m total length

Business Content & Required features3:46
Forecast credit card spending by analyzing customer demographics, transaction history, and time-based features to predict next month's total spending and guide personalized banking decisions.
Course Material0:01

Data Integration Architecture3:59
Understand the data integration architecture powering AI-powered banking analytics, from source systems through ETL in staging, data warehouse, data mart, and BI tools to machine learning deployment.
Data Source Understanding18:31
Explore how the dim customer, dim account, dim loan, fact transaction daily, fact card, and dim date tables connect via ERD to power AI-powered credit card spending forecasting in banking.
MySQL DWH DB Creation5:43
Create a data warehouse schema in MySQL Workbench and populate it with seven tables using SQL scripts, including dim customer, dim account, dim loan, dim date, daily, and fact card.
Apache Nifi - DWH Loading20:45
Demonstrates loading CSV data into a MySQL data warehouse with Apache NiFi. Build an ETL pipeline to populate dim and fact tables and validate results in MySQL Workbench.
DataMart DB Creation and Loading8:49
Create a datamart schema and final target table for monthly fact transactions, loading data from the data warehouse, with indexing, verification, and forecasting the next month’s spending.
Notebook - Import Libraries & DataSset7:15
Import pandas, numpy, matplotlib, seaborn, xgboost, and sqlalchemy to load and analyze data from a MySQL database. Merge datasets, compute loan-to-income, debt-to-income, and payment-to-balance ratios for forecasting and eda.
EDA & Visualization (Part 1)12:59
Explore exploratory data analysis and visualization of customer demographics, account types, and loan metrics to uncover spending patterns and inform AI-powered credit card forecasting in banking.
EDA & Visualization (Part 2)8:14
Explore data analysis of credit card spending and behavior using transaction monthly data and fact card data, with summary statistics, histograms, and discretionary versus necessity spending insights.
EDA & Visualization (Part 3)13:08
Analyze the summary data frame to compute statistics, inspect missing values, and visualize histograms, box plots, pairplots, time series trends, and random forest feature importance for next month spending.
LSTM Model21:45
Explore time series forecasting of credit card spending using LSTM, XGBoost, and artificial neural networks, with data preparation, feature engineering, training, evaluation, and forecasting.
XGBoost Algorithm Understanding8:22
Explore XGBoost, a powerful gradient boosting decision-tree ensemble with regularization for structured tabular data, and learn how sequential trees, residuals, and learning rate iterations improve predictions in regression and classification.
XGBoost Model Implementation7:17
Explore building and evaluating an XGBoost regressor for credit card spending forecasting, including training, testing, MAE and RMSE evaluation, and a function to forecast future months for a customer.
ANN Algorithm Understanding2:37
Explore artificial neural networks, including neurons, input, hidden, and output layers, and weights and biases. Master forward and backpropagation, activation functions, and loss measures, training with SGD or Adam.
ANN Model Implementation10:26
Develop an artificial neural network (ANN) in Keras for credit card spending forecasting using a three-layer model (64, 32, 1) with ReLU activations, minmax normalization, and Adam optimization.

Apache Nifi Installation5:35
Install Apache NiFi on Windows by verifying system requirements, installing Java, downloading NiFi, and running the NiFi server. Access the NiFi UI at localhost:8443 to design data flows.
Python & Jupyter Notebook Installation3:48
Install Anaconda from the official site and select Python 3.8, then follow Python installation steps from python.org for Windows, macOS, or Linux, and verify by running python.

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.

AI-Powered Credit Card Spending Forecasting in Banking

What you'll learn

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Course content

Introduction2 lectures • 4min

Credit Card Spending Forecasting14 lectures • 2hr 30min

Software Installation2 lectures • 9min

Requirements

Description

Who this course is for: