Bank of America Financial Complaints Overview

Project Summary:

This project provides an overview of financial complaints filed with Bank of America. The data is presented in a visually appealing and informative way, using charts and graphs to highlight key trends. The project also includes a breakdown of complaints by issue, product, state, media, and year.

Data Preparation:

• Acquired data: Extracted complaint data from various sources (e.g., Consumer Financial Protection Bureau website, internal customer feedback systems).
• Cleaning and validation: Identified and corrected inconsistencies, missing values, and formatting errors using Excel tools visually appealing and informative.
• Transformation: Transformed data into a consistent format suitable for analysis, employing techniques like date parsing and category creation.

Key Findings:

• 62,516 complaints were filed, with managing accounts being the most prevalent issue.
• Checking/savings accounts received the highest number of complaints, followed by credit cards and credit reporting.
• California had the most complaints, followed by Florida and Texas.
• Web referral was the most common filing method, followed by phone and postal mail.
• The response rate for complaints was 93.77%.

Visualizations:

• Interactive dashboards: Designed interactive dashboards in Power BI with insightful charts and graphs, allowing users to explore data from various dimensions.
• Drill-down capabilities: Enabled users to drill down into specific complaint categories, products, or regions for deeper analysis

Skills Used:

• Data preparation and cleaning
• Data modeling (star schema design)
• DAX functions for calculations and aggregations
• Data visualization in Power BI
• Communication and storytelling

Tools Used:

• Excel (data transformation)
• Power BI Desktop (data modeling, visualizations)

Conclusion:

By delving into 2023 Bank of America complaints with data preparation, modeling, and DAX analysis, I uncovered valuable insights. The visualizations highlighted critical areas such as managing accounts and credit card issues. I suggest focusing resources on enhancing customer support in these areas and improving communication around complaint resolution processes. Further analysis could explore correlations between complaint types and demographics to provide even more targeted solutions. This project demonstrates the power of data-driven analysis to improve customer experience and drive positive change.