Methodology for Visualizing Q4 2023 vs Q1 2024 CFPB Debt Collection Complaints by Service Members

Methodology For Cfpb Debt Collection Complaints

The objective of this study was to visualize debt collection complaints made by U.S. military service members, focusing on identifying the issues, frequencies, and alleged offenders mentioned in CFPB complaints related to various debt collection practices.

Data Collection

The data for this study was sourced from the Consumer Financial Protection Bureau (CFPB) complaint database. The dataset specifically targeted complaints filed by U.S. military service members, identified using the tag “Servicemember” within Q4 2023 and Q1 2024.

Complaint Issues

Our dataset includes various fields, such as the date of the complaint and specific tags related to the nature of the complaint. The complaint issues for this study were:

  1. Attempts to collect debts not owed
  2. Written notification about debt
  3. Took or threatened to take negative or legal action
  4. False statements or representation
  5. Communication tactics
  6. Threatened to contact someone or share information improperly
  7. Electronic communications

Data Processing

Data processing involved these steps to ensure accuracy and relevance:

  • Filtering: The dataset was filtered to include only complaints tagged as involving service members.
  • Dates: Complaints filed during Q4 2023 were compared to complaints filed in Q1 2024
  • Categorization: Complaints were categorized based on the specific issues mentioned.

Data Exclusions

To maintain the quality and reliability of the analysis, certain irrelevant or incomplete records were excluded from the dataset. These included entries lacking essential details, such as the names of credit bureaus in the debt complaint narratives. In debt collections, complaint narratives, references to credit bureau reports, and scores were omitted, since the CFPB maintains a separate category for “Credit reporting, credit repair services, or other personal consumer reports.”

Data Integration

The complaints data was integrated with additional contextual information where available, such as geographic location and the time of the complaint.

Data Visualization

The processed data was visualized using a choropleth map to represent the number of complaints across different states. The map used color gradients to show the density of complaints, with darker shades representing higher numbers of complaints. Python’s Folium library was used to create the interactive choropleth map, which allows users to hover over each state to view specific complaint figures.

  • Choropleth Map: Used to show the distribution of complaints across different states.
  • Modals: Used to show the breakdown of complaints, alleged offenders, and issues by state.


The accuracy and validity of the data were ensured by cross-referencing with other data sources to confirm consistency. Visual representations were tested manually for accuracy and clarity to ensure they effectively communicated the information.

Ethical Considerations

The study maintained the confidentiality and anonymity of the service members by not disclosing any personal information. The focus remained on the types of complaints and their frequencies, avoiding individual identities. By following this methodology, the study aimed to provide a clear, accurate, and insightful visualization of debt collection complaints made by U.S. military service members. This approach highlights areas needing attention and improvement in debt collection practices.