# CFPB Consumer Complaint Database Scraper (`automation-lab/cfpb-consumer-complaint-database-scraper`) Actor Export filtered CFPB complaint records for compliance, risk, CX, and competitive-intelligence workflows from the public CFPB database. - **URL**: https://apify.com/automation-lab/cfpb-consumer-complaint-database-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## CFPB Consumer Complaint Database Scraper Export filtered records from the official Consumer Financial Protection Bureau complaint database. ### What does CFPB Consumer Complaint Database Scraper do? CFPB Consumer Complaint Database Scraper downloads and parses the public CFPB Consumer Complaint Database feed and saves matching complaint records to an Apify dataset. It is built for recurring complaint monitoring, compliance reviews, market-risk research, and customer-experience intelligence. ### Who is it for? - 🏦 Bank compliance teams monitoring complaint patterns - 💳 Fintech risk teams tracking issues by product and company - 📊 Competitive-intelligence teams comparing complaint volume - 🧑‍⚖️ Legal and regulatory researchers exporting evidence sets - 🎧 CX leaders looking for recurring consumer pain points ### Why use this actor? The CFPB database is public, but the official bulk file is large. This actor streams the source file, applies filters, and returns structured records without you building CSV and ZIP processing code. ### Data source The actor uses the official CFPB public complaint bulk feed: `https://files.consumerfinance.gov/ccdb/complaints.csv.zip` ### What data can you extract? | Field | Description | | --- | --- | | `complaintId` | CFPB complaint identifier | | `dateReceived` | Date the CFPB received the complaint | | `product` | Financial product category | | `subProduct` | More specific product category | | `issue` | Complaint issue | | `subIssue` | More specific issue | | `company` | Company named in the complaint | | `state` | Consumer state | | `zipCode` | ZIP code where available | | `submittedVia` | Channel used to submit the complaint | | `companyResponse` | Company response status | | `timelyResponse` | Whether the company responded in time | | `consumerDisputed` | Historical disputed flag when present | | `consumerComplaintNarrative` | Optional public narrative text | ### How much does it cost to scrape CFPB complaints? This actor uses pay-per-event pricing. - A small start event is charged once per run. - A per-record event is charged for every complaint saved. - You control cost with `maxItems` and filters. ### Quick start 1. Open the actor on Apify. 2. Set `maxItems` to the number of complaint records you need. 3. Add optional company, product, issue, state, or date filters. 4. Run the actor. 5. Export the dataset as JSON, CSV, Excel, or via API. ### Input options #### Maximum complaints Use `maxItems` to cap the number of saved records. #### Company filters Use `companyContains` for broad matching such as `bank`, or `companyNames` for exact company names. #### Product filters Use `products` to include exact CFPB product categories. #### Issue filters Use `issues` to include exact CFPB issue names. #### State filters Use `states` with two-letter US state abbreviations. #### Date filters Use `dateReceivedFrom` and `dateReceivedTo` in `YYYY-MM-DD` format. #### Resolution filters Use `companyResponses`, `timelyResponse`, and `disputed` to focus on handling outcomes. #### Narrative option Set `includeNarrative` to true if you need the public complaint narrative text. ### Example input ```json { "maxItems": 100, "companyContains": "bank", "states": ["CA", "NY"], "dateReceivedFrom": "2025-01-01", "includeNarrative": false } ```` ### Example output ```json { "complaintId": "6779204", "dateReceived": "2023-03-31T16:04:06.000Z", "product": "Credit reporting, credit repair services, or other personal consumer reports", "issue": "Improper use of your report", "company": "CarMax, Inc.", "state": "NV", "submittedVia": "Web", "companyResponse": "Closed with explanation", "timelyResponse": true, "sourceUrl": "https://www.consumerfinance.gov/data-research/consumer-complaints/", "scrapedAt": "2026-07-02T00:00:00.000Z" } ``` ### Tips for better results - Start with a small `maxItems` while tuning filters. - Use exact product names when you already know CFPB categories. - Leave narrative disabled unless you need text analysis. - Combine company and state filters for regional monitoring. - Use date ranges to build periodic monitoring workflows. ### Compliance monitoring workflow Run the actor weekly with your company names, save the dataset to your BI tool, and compare issue/product trends over time. ### Competitive intelligence workflow Run the actor with competitor names and compare complaint categories, response status, and state distribution. ### CX research workflow Enable narratives for focused complaint sets and feed the output into classification, clustering, or sentiment-analysis tools. ### Integrations You can connect Apify datasets to Google Sheets, Make, Zapier, webhooks, S3, BigQuery, Snowflake, or your own API client. ### API usage #### Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/cfpb-consumer-complaint-database-scraper').call({ maxItems: 100, companyContains: 'bank' }); console.log(run.defaultDatasetId); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient('APIFY_TOKEN') run = client.actor('automation-lab/cfpb-consumer-complaint-database-scraper').call({ 'maxItems': 100, 'companyContains': 'bank', }) print(run['defaultDatasetId']) ``` #### cURL ```bash curl -X POST 'https://api.apify.com/v2/acts/automation-lab~cfpb-consumer-complaint-database-scraper/runs?token=APIFY_TOKEN' \ -H 'Content-Type: application/json' \ -d '{"maxItems":100,"companyContains":"bank"}' ``` ### MCP usage Use this actor from Claude Desktop or Claude Code through Apify MCP: `https://mcp.apify.com/?tools=automation-lab/cfpb-consumer-complaint-database-scraper` Claude Code setup: ```bash claude mcp add apify-cfpb-complaints "https://mcp.apify.com/?tools=automation-lab/cfpb-consumer-complaint-database-scraper" ``` Claude Desktop JSON config: ```json { "mcpServers": { "apify-cfpb-complaints": { "url": "https://mcp.apify.com/?tools=automation-lab/cfpb-consumer-complaint-database-scraper" } } } ``` Example prompts: - "Export 100 CFPB complaints mentioning bank in California." - "Get CFPB mortgage complaints with untimely responses." - "Find complaint records for a named fintech company." ### Performance notes The actor streams the CFPB ZIP and CSV instead of loading the full file into memory. Highly selective filters may need to scan many rows before enough matches are found. ### Limits The public source file is large. Keep `maxItems` realistic and use filters when possible. ### Legality The actor uses public government data from the CFPB. You are responsible for using exported data lawfully and respecting privacy, compliance, and data-retention requirements. ### FAQ #### Is CFPB complaint data public? Yes. The actor uses the official public CFPB Consumer Complaint Database bulk feed. #### Can I monitor one company every week? Yes. Save an input with `companyContains` or exact `companyNames`, then schedule the actor on Apify. ### Troubleshooting #### Why did the run scan many rows? Selective filters can require scanning a large portion of the source CSV before enough matches are found. #### Why are narratives missing? Narratives are only returned when CFPB publishes them and `includeNarrative` is enabled. ### Related scrapers - https://apify.com/automation-lab/sec-company-facts-api-scraper - https://apify.com/automation-lab/fdic-bankfind-scraper - https://apify.com/automation-lab/finra-disciplinary-actions-scraper ### Changelog #### 0.1 Initial version with streaming CFPB bulk CSV parsing, filters, and structured complaint output. ### Support If you need a new filter or output field, open an Apify issue on the actor page. ### Disclaimer This actor is not affiliated with or endorsed by the CFPB. ### Appendix: common filter ideas - Complaints by company and state - Complaints by mortgage product - Untimely response monitoring - Public narratives for text analysis - Competitor complaint comparison - Monthly compliance exports - Regional branch-risk review - Product launch complaint monitoring - Complaint response quality audit - Complaint IDs for downstream enrichment # Actor input Schema ## `maxItems` (type: `integer`): Maximum number of matching complaint records to save. ## `companyContains` (type: `string`): Case-insensitive text contained in the company name, for example 'bank of america'. ## `companyNames` (type: `array`): Optional exact company names to match. Leave empty to match any company or use the contains field above. ## `products` (type: `array`): Exact CFPB product names such as 'Credit reporting or other personal consumer reports' or 'Mortgage'. ## `issues` (type: `array`): Exact CFPB issue names to include. ## `states` (type: `array`): Two-letter US state abbreviations, for example CA, NY, TX. ## `dateReceivedFrom` (type: `string`): Earliest complaint received date in YYYY-MM-DD format. ## `dateReceivedTo` (type: `string`): Latest complaint received date in YYYY-MM-DD format. ## `companyResponses` (type: `array`): Exact company response values such as 'Closed with explanation'. ## `submittedVia` (type: `array`): Exact submission channels such as Web, Referral, Phone, Postal mail, Fax, or Email. ## `timelyResponse` (type: `boolean`): Filter by CFPB timely-response flag. Leave empty to include both timely and non-timely responses. ## `disputed` (type: `string`): Filter the historical disputed flag when present. Newer complaints may have this blank/unknown. ## `includeNarrative` (type: `boolean`): Include the public complaint narrative text when CFPB provides it. Disable for smaller exports. ## Actor input object example ```json { "maxItems": 20, "companyNames": [], "products": [], "issues": [], "states": [], "companyResponses": [], "submittedVia": [], "disputed": "any", "includeNarrative": false } ``` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "maxItems": 20, "companyContains": "", "companyNames": [], "products": [], "issues": [], "states": [], "dateReceivedFrom": "", "dateReceivedTo": "", "companyResponses": [], "submittedVia": [], "disputed": "any", "includeNarrative": false }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/cfpb-consumer-complaint-database-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "maxItems": 20, "companyContains": "", "companyNames": [], "products": [], "issues": [], "states": [], "dateReceivedFrom": "", "dateReceivedTo": "", "companyResponses": [], "submittedVia": [], "disputed": "any", "includeNarrative": False, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/cfpb-consumer-complaint-database-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxItems": 20, "companyContains": "", "companyNames": [], "products": [], "issues": [], "states": [], "dateReceivedFrom": "", "dateReceivedTo": "", "companyResponses": [], "submittedVia": [], "disputed": "any", "includeNarrative": false }' | apify call automation-lab/cfpb-consumer-complaint-database-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/cfpb-consumer-complaint-database-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CFPB Consumer Complaint Database Scraper", "description": "Export filtered CFPB complaint records for compliance, risk, CX, and competitive-intelligence workflows from the public CFPB database.", "version": "0.1", "x-build-id": "01C9lxm800N1L7Eod" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~cfpb-consumer-complaint-database-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-cfpb-consumer-complaint-database-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~cfpb-consumer-complaint-database-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-cfpb-consumer-complaint-database-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~cfpb-consumer-complaint-database-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-cfpb-consumer-complaint-database-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "maxItems": { "title": "Maximum complaints", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of matching complaint records to save.", "default": 20 }, "companyContains": { "title": "Company name contains", "type": "string", "description": "Case-insensitive text contained in the company name, for example 'bank of america'." }, "companyNames": { "title": "Exact company names", "type": "array", "description": "Optional exact company names to match. Leave empty to match any company or use the contains field above.", "items": { "type": "string" } }, "products": { "title": "Products", "type": "array", "description": "Exact CFPB product names such as 'Credit reporting or other personal consumer reports' or 'Mortgage'.", "items": { "type": "string" } }, "issues": { "title": "Issues", "type": "array", "description": "Exact CFPB issue names to include.", "items": { "type": "string" } }, "states": { "title": "States", "type": "array", "description": "Two-letter US state abbreviations, for example CA, NY, TX.", "items": { "type": "string" } }, "dateReceivedFrom": { "title": "Date received from", "type": "string", "description": "Earliest complaint received date in YYYY-MM-DD format." }, "dateReceivedTo": { "title": "Date received to", "type": "string", "description": "Latest complaint received date in YYYY-MM-DD format." }, "companyResponses": { "title": "Company responses", "type": "array", "description": "Exact company response values such as 'Closed with explanation'.", "items": { "type": "string" } }, "submittedVia": { "title": "Submitted via", "type": "array", "description": "Exact submission channels such as Web, Referral, Phone, Postal mail, Fax, or Email.", "items": { "type": "string" } }, "timelyResponse": { "title": "Timely response only?", "type": "boolean", "description": "Filter by CFPB timely-response flag. Leave empty to include both timely and non-timely responses." }, "disputed": { "title": "Consumer disputed", "enum": [ "any", "yes", "no", "unknown" ], "type": "string", "description": "Filter the historical disputed flag when present. Newer complaints may have this blank/unknown.", "default": "any" }, "includeNarrative": { "title": "Include consumer complaint narrative", "type": "boolean", "description": "Include the public complaint narrative text when CFPB provides it. Disable for smaller exports.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```