Bank of America Financial Consumer Complaints Analysis

In my current role, I primarily use hashtag#Amazon hashtag#QuickSight for data visualization, and as a result, I haven't worked with Microsoft Power BI for some time. Over the weekend, I decided to work a project in Microsoft Power BI to solidify my skills. Here are the data cleaning steps I took in Power Query before addressing the problem statements:

Data Cleaning Steps :

-- Sorted datatypes appropriately.

-- Checked for and removed duplicate values.

-- Used the Trim function where necessary to clean up text data.

-- Utilized the Value and Replace functions for further data transformation.

-- Created the required Calculated measures and Columns

-- Created an additional Date Table and established relationships in the data model.-- Removed irrelevant columns to streamline the dataset.

Problem Statements

1. Do consumer complaints show any seasonal patterns?
   

2. Which products present the most complaints? What are its most common issues?
   

3. How are complaints typically resolved?
   

4. Can you learn anything from the complaints with untimely responses?
   

Insights Summary:

-- Seasonal Patterns: Consumer complaints peak in Q3 and decline significantly in Q4, suggesting seasonal influences.

-- Most Complaints by Product: Checking and savings accounts receive the most complaints with issues revolving around account management, while student loans receive the least.-- Complaint Resolutions: 65% of complaints are resolved with explanations, and 0.013% are closed without explanations.

-- Untimely Responses: 3.8% of complaints are responded to untimely, with the majority from web complaint sources, cutting across products like checking or savings accounts, credit or prepaid cards, and credit reporting and repair services.