metadata
license: cc0-1.0
task_categories:
- tabular-regression
- time-series-forecasting
tags:
- cfpb
- consumer-finance
- credit-card
- complaints
- finance
pretty_name: Consumer Harm CFPB Summary
size_categories:
- 1K<n<10K
Consumer Harm — CFPB Summary
Aggregated CFPB Consumer Complaint Database summaries for the Consumer Harm story dashboard (credit cards: relief vs. the void).
Study windows (fixed calendar)
| Window | Range |
|---|---|
| Study | 2011-01-01 → 2024-12-31 |
| Early comparison | 2011–2017 |
| Recent comparison | 2018–2024 |
About 7.2 million complaints fall in the study window (built from the full CFPB bulk CSV).
Contents
| File | Description |
|---|---|
cfpb_summary.db |
Complete SQLite aggregate DB (~4 MB) |
SOURCE.txt |
Download provenance notes (when present) |
Tables in cfpb_summary.db
| Table | Rows |
|---|---|
channel_by_product |
112 |
credit_card_companies |
1,107 |
credit_card_company_monthly |
13,456 |
credit_card_issue_monthly |
3,778 |
credit_card_issue_response |
299 |
credit_card_issues |
58 |
credit_card_monthly |
157 |
credit_card_states |
64 |
credit_card_subissue_monthly |
7,745 |
credit_card_subissues |
124 |
credit_card_subproducts |
8 |
credit_card_theme_monthly |
878 |
meta |
11 |
product_monthly |
1,458 |
product_stats |
21 |
response_by_product |
108 |
Build meta
{'study_start': '2011-01-01', 'study_end': '2024-12-31', 'early_end': '2017-12-31', 'recent_start': '2018-01-01', 'study_label': '2011–2024', 'early_label': '2011–2017', 'recent_label': '2018–2024', 'period_mode': 'fixed_calendar', 'built_at': '2026-07-25T17:32:35.644409', 'total_complaints': '7231945', 'source_csv': '/home/grimdolf/opportunity_harm/cfpb/extracted/complaints.csv'}
What is not included
The raw ~8 GB complaints.csv is omitted — download from CFPB. This dataset is the analysis-ready summary used in production.
Live demo
- Landing: https://consumer-harm.vercel.app
- Dashboard: https://consumer-harm-production.up.railway.app
- Code: https://github.com/tedrubin80/consumer-harm
Load example
import sqlite3
import pandas as pd
conn = sqlite3.connect("cfpb_summary.db")
products = pd.read_sql("SELECT * FROM product_stats", conn)
cc = pd.read_sql("SELECT * FROM credit_card_companies ORDER BY complaints DESC LIMIT 20", conn)
License
Aggregates packaged as CC0. Underlying CFPB data remains subject to CFPB terms of use.