Top 3 Data Analytics Project Ideas in Banking – Credit Card Division

Data Analytics Project Ideas are essential for students, professionals, and enthusiasts who want to apply theoretical knowledge to real-world problems. These projects help develop practical skills in data cleaning, visualization, statistical analysis, and predictive modeling. Whether you are aiming to enhance your resume or gain hands-on experience, selecting the right project idea is a crucial step in your data analytics journey.

Exploring various data analysis project ideas allows individuals to work with real datasets, uncover insights, and make data-driven decisions. From analyzing customer behavior to forecasting sales trends or visualizing public health data, these projects not only improve technical proficiency but also build analytical thinking and problem-solving skills that are valuable in today’s data-driven world.

Project Goal & Scope

In this Project I aim to complete 3 top-tier projects in the Credit Card division of banking analytics using Excel and Power Query. Each project will be based on a dataset of 500 records and showcase automation, insight generation, and dashboard creation. You can later feature these projects on your website and LinkedIn to establish your data analyst profile.

Dataset Summary

The dataset contains 500 synthetic records with 20+ variables such as Age, Gender, Income, Credit_Limit, Balance_Amount, Monthly_Spend, Transaction_Count, and more. A column for Utilization_Ratio was added to enhance the analysis. The data is saved in Excel and used in project.

🔹 Project 1: Credit Card Customer Segmentation

Objective: Identify and categorize credit card customers based on their spending behavior, credit limit, and payment patterns for targeted marketing and risk assessment.

Skills Used:

  • Data transformation with Power Query

  • Statistical segmentation (e.g., K-means logic in Excel)

  • Dashboard to visualize customer segments

Goal: Segment customers into groups based on spending, credit limit, and utilization.

🔧 Steps:

  1. Variables Used:

    • Monthly_Spend

    • Credit_Limit

    • Utilization_Ratio (%)

    • Transaction_Count

  2. Power Query Tasks:

    • Remove duplicates

    • Filter out customers with missing values

    • Normalize key metrics (optional using manual scaling in Excel)

  3. Excel Formulas:

    • Average Spend per Segment:
      =AVERAGEIFS(Monthly_Spend, Segment_Column, "Segment 1")

    • Utilization Analysis:
      =IF([Utilization_Ratio (%)]>=70,"High",IF([Utilization_Ratio (%)]>=40,"Medium","Low"))

  4. Suggested Segments (use IF statements):

    • High Spender: Monthly_Spend > $3000

    • Low Utilization: Utilization_Ratio < 30%

    • Frequent User: Transaction_Count > 30

  5. Dashboard Charts:

    • Pie chart of customer segments

    • Bar chart of average spend per segment

    • Region vs Spend heatmap

Building Dashboards in Excel

  • Slicers: Region, Card_Type, Utilization_Category

  • KPI Cards:

    • Total Customers

    • Avg Monthly Spend

    • Avg Utilization

  • Charts:

    • Pie Chart: % of High/Medium/Low Utilization

    • Bar Chart: Avg Spend by Segment

    • Line Chart: Spend vs Transaction Count