icon: star-sharp-half-stroke
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This section covers the usage of the Consumer Financial Complaint
ticker-mapped dataset.
CFPB Complaints
Data Notice: This dataset provides academic research access with a 6-month data lag. For real-time data access, please visit sov.ai to subscribe. For market insights and additional subscription options, check out our newsletter at blog.sov.ai.
from datasets import load_dataset
df_complaints = load_dataset("sovai/cfpb_complaints", split="train").to_pandas().set_index(["ticker","date"])
Data is updated weekly as data arrives after market close US-EST time.
Tutorials are the best documentation — Consumer Financial Complaints Tutorial
| Category | Details |
|---|---|
| Input Datasets | CFPB Filings |
| Models Used | LLMs, Parsing, Risk Scoring |
| Model Outputs | CFPB Risk Scores |
Description
This dataset provides detailed information on consumer complaints filed against financial institutions, mapped to company ticker symbols. It includes data on complaint types, company responses, and resolution status, along with derived risk scores.
This data enables analysis of consumer sentiment, regulatory compliance, and potential risks across different financial companies and products.
Data Access
import sovai as sov
df_complaints = sov.data("complaints/public")
Accessing Specific Tickers
You can also retrieve data for specific tickers. For example:
df_ticker_complaints = sov.data("complaints/public", tickers=["WFC", "EXPGY"])
Data Dictionary
| Column Name | Description |
|---|---|
| ticker | Stock ticker symbol of the company |
| date | Date the complaint was received |
| company | Name of the company the complaint is against |
| bloomberg_share_id | Bloomberg Global Share Class Level Identifier |
| culpability_score | Score indicating the company's culpability in the complaint |
| complaint_score | Score based on the severity of the complaint |
| grievance_score | Score based on the grievance level of the complaint |
| total_risk_rating | Overall risk rating combining culpability, complaint, and grievance scores |
| product | Financial product related to the complaint |
| sub_product | Specific sub-category of the financial product |
| issue | Main issue of the complaint |
| sub_issue | Specific sub-category of the issue |
| consumer_complaint_narrative | Narrative description of the complaint provided by the consumer |
| company_public_response | Public response provided by the company |
| state | State where the complaint was filed |
| zip_code | ZIP code of the consumer |
| tags | Any tags associated with the complaint (e.g., "Servicemember") |
| consumer_consent_provided | Indicates if the consumer provided consent for sharing details |
| submitted_via | Channel through which the complaint was submitted |
| date_sent_to_company | Date the complaint was sent to the company |
| company_response_to_consumer | Type of response provided by the company to the consumer |
| timely_response | Indicates if the company responded in a timely manner |
| consumer_disputed | Indicates if the consumer disputed the company's response |
| selected_name | Name used for company matching |
| similarity | Similarity score for company name matching |
Use Cases
- Risk Assessment: Evaluate the risk profile of financial institutions based on complaint data.
- Consumer Sentiment Analysis: Analyze consumer sentiment towards different financial products and companies.
- Regulatory Compliance: Monitor compliance issues and identify potential regulatory risks.
- Product Performance Evaluation: Assess the performance and issues related to specific financial products.
- Competitive Analysis: Compare complaint profiles across different financial institutions.
- Geographic Trend Analysis: Identify regional trends in financial complaints.
- Customer Service Improvement: Identify areas for improvement in customer service based on complaint types and resolutions.
- ESG Research: Incorporate complaint data into Environmental, Social, and Governance (ESG) assessments.
- Fraud Detection: Identify patterns that might indicate fraudulent activities.
- Policy Impact Assessment: Evaluate the impact of policy changes on consumer complaints over time.
The resulting dataset provides a comprehensive view of consumer complaints in the financial sector, enabling detailed analysis of company performance, consumer issues, and regulatory compliance.