1. Abstract

The banking industry generates large volumes of customer data that can be analysed to understand customer behavior and financial risk. Credit card services are widely used, and banks must analyse customer profiles before launching new credit card services or approving credit limits. Data analysis helps banks identify risk factors such as delayed payments, borrowing patterns, and potential defaults.

This project focuses on analysing a credit card customer dataset using Power BI. The dataset contains information about customer demographics, credit limits, repayment status, bill amounts, payment history, and default status. The objective is to study customer financial behavior and identify patterns related to credit card usage and repayment performance.

The dataset is first explored and cleaned using Power Query Editor in Power BI. Data preprocessing includes correcting categorical values, renaming columns, grouping categories, and transforming numeric codes into meaningful labels. After cleaning the dataset, various visualizations and dashboards are created to analyse repayment behavior, borrowing patterns, and default trends.

This project demonstrates how business intelligence tools like Power BI can be used to analyse financial datasets, helping banks better understand customer credit behavior and potential risk factors.

2. Objectives

The main objectives of this project are:

1. To analyse credit card customer data using Power BI.
2. To understand customer profiles including age, education, marital status, and gender.
3. To analyse repayment behavior of credit card customers.
4. To identify delayed payments and repayment patterns.
5. To analyse credit card bill amounts and payment amounts.
6. To identify potential credit card defaulters.
7. To perform data cleaning and transformation using Power Query Editor.
8. To build interactive dashboards for analysing financial data.

3. Existing System

In traditional banking systems, credit card data analysis is often performed using basic tools such as spreadsheets or static reports. Analysts manually examine customer information and payment records to identify risk factors.

Limitations of Existing System

1. Manual data analysis is time-consuming.
2. Difficult to analyse large volumes of financial data.
3. Limited visualization capabilities.
4. Hard to identify hidden patterns in repayment behavior.
5. Static reports do not allow interactive data exploration.

These limitations make it necessary to use modern business intelligence tools like Power BI for efficient analysis.

4. Proposed System

The proposed system uses Power BI to analyse and visualize credit card customer data.

In this system:

1. Credit card customer data is imported into Power BI.
2. Data is cleaned and transformed using Power Query Editor.
3. Categorical variables such as education, marital status, and repayment status are converted into readable categories.
4. Column names are modified for better understanding.
5. Visualizations are created to analyse credit limits, bill amounts, payment history, and repayment delays.
6. Interactive dashboards allow analysts to explore customer credit behavior and default risk.

This system helps banks better understand customer financial activity and improve decision-making for credit card services.

1. Implementation Procedure

The implementation process includes the following steps:

Step 1: Data Collection

The credit card dataset is collected from the UCI Machine Learning Repository. It contains detailed information about credit card customers and their financial activities.

 Step 2: Data Loading

The dataset is imported into Power BI Desktop. The Transform Data option is used to open the Power Query Editor for preprocessing.

 Step 3: Data Understanding

The dataset contains several important variables including:

1. Customer ID – Unique identifier for each customer
2. Limit Balance – Credit limit provided by the bank
3. Gender (Sex) – Gender of the customer
4. Education – Education level of the customer
5. Marriage – Marital status of the customer
6. Age – Age of the customer
7. Repayment Status (PAY columns) – Payment status from April to September 2005
8. Bill Amount – Credit card bill amounts for previous months
9. Pay Amount – Amount paid back to the bank by customers
10. Default Payment Next Month – Indicates whether the customer defaulted

Step 4: Data Cleaning

Data preprocessing includes:

1. Replacing numerical categorical values with meaningful labels
2. Combining similar categories into one group
3. Handling missing or unnecessary values
4. Renaming columns for better understanding

For example:

1. Education categories are grouped into Graduate School, University, High School, and Others.
2. Marital status categories are converted into Married, Single, and Others.

 Step 5: Data Transformation

Column names and data formats are improved:

1. Repayment status columns are renamed based on months.
2. Bill amount columns are renamed according to billing periods.
3. Payment amount columns are organized by month.

This improves readability and analysis.

 Step 6: Data Visualization

Various visualizations are created in Power BI such as:

1. Bar charts showing credit limits by customer groups
2. Line charts representing repayment trends over months
3. Charts analysing bill amounts and payment amounts
4. Visualizations identifying customers with delayed payments

Step 7: Dashboard Creation

An interactive dashboard is created including:

1. Customer demographic analysis
2. Credit limit analysis
3. Repayment delay analysis
4. Default risk analysis

The dashboard helps users easily explore financial patterns and customer credit behavior.

6. Software Requirements

The software used in this project includes:

1. Power BI Desktop – Data analysis and visualization
2. Power Query Editor – Data cleaning and transformation
3. Microsoft Excel / CSV files – Dataset format
4. Windows Operating System
5. Hardware Requirements

Minimum hardware requirements:

1. Processor: Intel i3 or higher
2. RAM: 4 GB minimum (8 GB recommended)
3. Storage: 256 GB or higher
4. System: Laptop or Desktop computer

8. Advantages of the Project

1. Helps analyse customer credit behavior effectively.
2. Identifies customers with delayed payments.
3. Helps banks detect potential credit card defaulters.
4. Provides interactive dashboards for financial analysis.
5. Simplifies complex financial data through visualizations.
6. Supports data-driven decision making in banking.
7. Demonstrates practical application of Power BI in financial analytics.