CFPB Consumer Complaint Search

Search and extract consumer complaint data from the Consumer Financial Protection Bureau (CFPB) database. Access over 5 million complaints filed against financial companies in the United States since 2011, with filtering by keyword, company, product type, state, and date range. Two output modes: records for individual complaints, aggregation for top-N leaderboards (most-complained-about companies, top issues, geographic distribution).

The CFPB complaint database is the largest publicly available source of consumer grievances related to financial products and services in the US, maintained by a federal government agency. Essential for compliance screening, AML due diligence, KYC risk assessment, and regulatory monitoring. This actor connects to the official CFPB public API and transforms raw Elasticsearch-style responses into clean, structured JSON records -- ready for analysis, compliance workflows, or integration into data pipelines.

Whether you are a compliance officer tracking complaints against a specific bank, a data journalist investigating patterns in consumer harm, or a fintech company benchmarking competitor reputation, this actor gives you programmatic access to CFPB data without managing API pagination, rate limiting, or data normalization yourself.

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Why use CFPB Consumer Complaint Search?

• No API key required -- The CFPB API is a free US government data source. No authentication, no signup, no usage limits.
• Clean structured output -- Raw CFPB responses use nested Elasticsearch formatting with inconsistent field names. This actor normalizes every record into a flat, consistent JSON schema.
• Two output modes -- Records mode returns one row per complaint. Aggregation mode returns top-N count buckets so you can answer "which companies received the most complaints in 2024?" in one call instead of paginating through millions of records.
• Up to 10,000 records per run -- Bumped from the previous 500-row cap. Auto-pagination handles the entire matching result set within Elasticsearch's 10,000-row single-query limit.
• Cloud scheduling -- Run searches on a recurring schedule using Apify's built-in scheduler to monitor new complaints daily, weekly, or at any interval.
• No infrastructure needed -- Runs entirely on Apify's cloud. No servers, no Docker setup, no dependencies to install.
• Direct complaint links -- Every record-mode result includes a generated URL linking directly to the complaint detail page on the CFPB website.

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Key features

• Search by keyword across complaint narratives and all indexed fields
• Filter by company name (exact match -- e.g., "Bank of America", "Equifax")
• Filter by financial product type: credit reporting, debt collection, mortgages, credit cards, student loans, and 5 more categories
• Filter by US state using standard two-letter codes (CA, NY, TX, etc.)
• Filter by date range to isolate complaints within a specific time window
• Sort results by newest first, oldest first, most relevant, or least relevant
• Retrieve up to 10,000 complaints per run with built-in pagination (100 per page)
• Aggregation mode — group complaints by company, product, issue, sub-issue, state, response type, submission channel, tags, or timely-response flag. Returns top-N count buckets with rank, count, and share of total.
• Auto-pagination flag — fetch every matching complaint up to the 10,000 cap in one run
• Consumer narratives included when the consumer opted in to public disclosure
• Direct CFPB URL generated for each complaint record
• 200ms delay between API pages to respect the government server

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How to use

Apify Console

1. Go to the CFPB Consumer Complaint Search actor page on Apify.
2. Click Start to open the input configuration.
3. Enter a search term, company name, or select a product type. You can combine multiple filters.
4. Optionally set a date range and state to narrow results.
5. Choose your sort order and maximum number of results.
6. Click Start to run the actor.
7. When finished, download results from the Dataset tab in JSON, CSV, Excel, or other formats.

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/cfpb-consumer-complaints").call(run_input={
    "searchTerm": "unauthorized charges",
    "company": "Wells Fargo",
    "product": "Checking or savings account",
    "state": "CA",
    "dateFrom": "2024-01-01",
    "dateTo": "2024-12-31",
    "sortBy": "created_date_desc",
    "maxResults": 100,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['complaintId']} | {item['company']} | {item['issue']}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/cfpb-consumer-complaints").call({
    searchTerm: "identity theft",
    company: "EQUIFAX, INC.",
    sortBy: "created_date_desc",
    maxResults: 50,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Found ${items.length} complaints`);
items.forEach((item) => console.log(`${item.complaintId}: ${item.issue}`));

---

Input parameters

Parameter	Type	Required	Default	Description
searchTerm	String	No	--	Keyword search across complaint narratives and fields (e.g., "credit report", "late fee", "identity theft")
company	String	No	--	Filter by company name (e.g., "Bank of America", "Wells Fargo", "Equifax")
product	Select	No	Any	Financial product type: credit reporting, debt collection, mortgage, credit card, checking/savings, student loan, vehicle loan, money transfer, payday loan, personal loan
state	String	No	--	US state code, automatically uppercased (e.g., "CA", "NY", "TX")
dateFrom	String	No	--	Start date for complaints received (YYYY-MM-DD format)
dateTo	String	No	--	End date for complaints received (YYYY-MM-DD format)
sortBy	Select	No	created_date_desc	Sort order: Newest first, Oldest first, Most relevant, Least relevant. Records mode only.
maxResults	Integer	No	50	Maximum complaints to return (1--10000). In aggregation mode, caps how many buckets are emitted.
outputMode	Select	No	records	records (one row per complaint) or aggregation (top-N count buckets)
aggregateBy	Select	No	company	When outputMode=aggregation: company, product, sub_product, issue, sub_issue, state, company_response, submitted_via, tags, or timely
autoPaginate	Boolean	No	false	When true and total matching > maxResults, fetch every matching complaint up to the 10,000 single-query cap. Records mode only.

Example input — records mode (JSON)

{
    "outputMode": "records",
    "searchTerm": "credit report",
    "company": "EQUIFAX, INC.",
    "product": "Credit reporting, credit repair services, or other personal consumer reports",
    "state": "FL",
    "dateFrom": "2024-06-01",
    "dateTo": "2024-12-31",
    "sortBy": "created_date_desc",
    "maxResults": 100
}

Example input — aggregation mode (top companies in 2024)

{
    "outputMode": "aggregation",
    "aggregateBy": "company",
    "dateFrom": "2024-01-01",
    "dateTo": "2024-12-31",
    "maxResults": 25
}

Tips: Combine multiple filters for precise results. Use date ranges for trend analysis. When using a keyword search in records mode, try sorting by "Most relevant first" for best matches. For "top N" leaderboard questions over millions of complaints, use aggregation mode — one HTTP call instead of thousands. If no filters are set, the actor returns the most recent complaints (records mode) or the global top buckets (aggregation mode).

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Output

Every row carries a kind discriminator: "record" for individual complaints, "aggregation" for count buckets. Filter kind === "aggregation" downstream to isolate leaderboard rows.

Example output — records mode (JSON)

{
    "kind": "record",
    "complaintId": "8234561",
    "dateReceived": "2024-11-15",
    "dateSentToCompany": "2024-11-15",
    "product": "Credit reporting, credit repair services, or other personal consumer reports",
    "subProduct": "Credit reporting",
    "issue": "Incorrect information on your report",
    "subIssue": "Information belongs to someone else",
    "company": "EQUIFAX, INC.",
    "companyResponse": "Closed with explanation",
    "companyPublicResponse": "Company has responded to the consumer and the CFPB and chooses not to provide a public response",
    "state": "FL",
    "zipCode": "331XX",
    "narrative": "I discovered several accounts on my Equifax credit report that do not belong to me. I have filed disputes multiple times but the inaccurate information remains.",
    "consumerDisputed": "N/A",
    "timely": "Yes",
    "submittedVia": "Web",
    "cfpbUrl": "https://www.consumerfinance.gov/data-research/consumer-complaints/search/detail/8234561",
    "extractedAt": "2025-06-10T14:32:08.123Z"
}

Example output — aggregation mode (JSON)

Top companies by complaint count for 2024 (aggregateBy: "company", dateFrom: "2024-01-01", dateTo: "2024-12-31"):

[
    { "kind": "aggregation", "aggregateBy": "company", "rank": 1, "term": "TRANSUNION INTERMEDIATE HOLDINGS, INC.", "count": 790668, "share": 0.2891, "totalMatching": 2734293, "queryFilters": { "searchTerm": null, "company": null, "product": null, "state": null, "dateFrom": "2024-01-01", "dateTo": "2024-12-31" }, "extractedAt": "2026-05-08T10:00:00.000Z" },
    { "kind": "aggregation", "aggregateBy": "company", "rank": 2, "term": "EXPERIAN INFORMATION SOLUTIONS INC.", "count": 754851, "share": 0.2761, "totalMatching": 2734293, "queryFilters": { "searchTerm": null, "company": null, "product": null, "state": null, "dateFrom": "2024-01-01", "dateTo": "2024-12-31" }, "extractedAt": "2026-05-08T10:00:00.000Z" },
    { "kind": "aggregation", "aggregateBy": "company", "rank": 3, "term": "EQUIFAX, INC.", "count": 752722, "share": 0.2753, "totalMatching": 2734293, "queryFilters": { "searchTerm": null, "company": null, "product": null, "state": null, "dateFrom": "2024-01-01", "dateTo": "2024-12-31" }, "extractedAt": "2026-05-08T10:00:00.000Z" }
]

Output fields — records mode

Field	Type	Description
kind	String	Always "record" in records mode
complaintId	String	Unique CFPB complaint identifier
dateReceived	String	Date the CFPB received the complaint (YYYY-MM-DD)
dateSentToCompany	String	Date the complaint was forwarded to the company
product	String	Financial product or service category
subProduct	String	More specific product subcategory
issue	String	Primary issue described in the complaint
subIssue	String	More specific issue detail
company	String	Name of the company the complaint is against
companyResponse	String	How the company responded (e.g., "Closed with explanation", "Closed with monetary relief")
companyPublicResponse	String	The company's optional public-facing response text
state	String	Two-letter US state code where the consumer is located
zipCode	String	Consumer's ZIP code (may be partially redacted)
narrative	String/null	Consumer's written description of the complaint, or null if not provided or consent not given
consumerDisputed	String	Whether the consumer disputed the company's response
timely	String	Whether the company responded in a timely manner ("Yes" or "No")
submittedVia	String	Submission channel (Web, Phone, Referral, Postal mail, Fax, Email)
cfpbUrl	String	Direct link to the complaint on the CFPB website
extractedAt	String	ISO 8601 timestamp of when the data was extracted

Output fields — aggregation mode

Field	Type	Description
kind	String	Always "aggregation" in aggregation mode
aggregateBy	String	Field used for grouping (e.g., "company", "product", "state")
rank	Integer	1-based rank within the result set (1 = largest count)
term	String	The bucket label (e.g., "EQUIFAX, INC.", "Mortgage", "CA")
count	Integer	Number of complaints in this bucket
share	Number	count / totalMatching — fraction of all matching complaints in this bucket (0..1)
totalMatching	Integer	Total complaints matching the query filters across all buckets
queryFilters	Object	Echo of the filters that produced this aggregation, for traceability
extractedAt	String	ISO 8601 timestamp of when the aggregation was computed

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Use cases

• Most-complained-about leaderboards (aggregation mode) -- "Top 25 companies by complaint count in 2024", "Top 10 issues for credit cards in CA", "State-by-state complaint distribution for mortgages". One call. No raw-record paging.
• Compliance monitoring -- Track complaints filed against your own institution or competitors on a daily or weekly schedule to catch emerging issues early.
• Due diligence -- Before partnering with, acquiring, or investing in a financial company, review their complaint history for red flags.
• Investigative journalism -- Analyze complaint trends by product type, geography, or time period to uncover systemic consumer harm patterns.
• Legal research -- Find complaints related to specific issues like identity theft, unauthorized charges, predatory lending, or discriminatory practices.
• Market intelligence -- Identify where consumers are most dissatisfied to spot product improvement or market entry opportunities.
• Regulatory risk assessment -- Monitor complaint volumes and company response patterns as leading indicators of potential regulatory action.
• Academic research -- Build datasets for studying consumer finance behavior, complaint resolution effectiveness, or financial inclusion topics.
• Brand reputation tracking -- Track how a company's complaint volume and resolution quality changes over time relative to industry peers.
• Geographic analysis -- Compare complaint patterns across states to identify regional concentrations of specific financial product issues. Aggregation mode with aggregateBy: "state" returns the full distribution in one call.
• Customer experience benchmarking -- Compare timeliness and resolution types across companies in the same product category. Use aggregateBy: "company_response" or aggregateBy: "timely" for cohort-level metrics.

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API & integration

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("OuMDkYU3IROUS0AEj").call(run_input={
    "company": "Bank of America",
    "product": "Mortgage",
    "dateFrom": "2024-01-01",
    "maxResults": 200,
})

dataset = client.dataset(run["defaultDatasetId"])
for item in dataset.iterate_items():
    print(f"{item['dateReceived']} - {item['issue']} ({item['companyResponse']})")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("OuMDkYU3IROUS0AEj").call({
    searchTerm: "late fee",
    product: "Credit card or prepaid card",
    state: "NY",
    maxResults: 75,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(JSON.stringify(items, null, 2));

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/OuMDkYU3IROUS0AEj/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchTerm": "debt collection harassment",
    "state": "TX",
    "sortBy": "created_date_desc",
    "maxResults": 50
  }'

# Fetch results from the dataset (use defaultDatasetId from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

Platform integrations

• Zapier -- Trigger workflows when new complaint data is collected. Push to Google Sheets, Slack, email, or CRMs.
• Make (Integromat) -- Build multi-step automations combining CFPB data with other sources.
• Google Sheets -- Export results directly to a spreadsheet for collaborative analysis.
• Webhooks -- Receive HTTP callbacks when actor runs complete to trigger downstream processing.
• Apify Dataset API -- Access results in JSON, CSV, XML, Excel, HTML, or RSS formats.

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How it works

Records mode

1. The actor reads your input configuration (search term, company, product, state, date range, sort order, max results, autoPaginate flag).
2. It constructs a query URL for the CFPB Consumer Complaint API with pagination parameters (size=100, frm=0, no_aggs=true).
3. The actor sends an HTTP GET request to the CFPB API endpoint.
4. The Elasticsearch-style response (hits.hits) is parsed and each complaint record is transformed from the raw _source format into a clean flat JSON object.
5. Transformed records are pushed to the Apify dataset.
6. If more results are needed, the actor increments the offset and fetches the next page after a 200ms delay.
7. When autoPaginate=true and the total matching count exceeds maxResults, the actor extends the run to fetch every matching record up to the 10,000 single-query cap.
8. Steps 3--6 repeat until the requested count is collected or no more results are available.

Aggregation mode

1. The actor reads your filters and the aggregateBy field selection.
2. It constructs a single query URL with size=0&no_aggs=false&field=all.
3. CFPB returns the bucket counts for every aggregatable field in one response.
4. The actor extracts the buckets for the chosen aggregateBy field, ranks them by count (largest first), computes share = count / totalMatching, and emits one row per bucket up to maxResults.
5. The result is a ready-to-graph leaderboard — no client-side counting required.

CFPB Consumer Complaint Search Pipeline

  Input Config         CFPB API                Transform              Apify Dataset
 +------------+    +-----------------+    +------------------+    +---------------+
 | outputMode |    | records mode:   |    | hits -> flat JSON |    | kind=record   |
 | aggregateBy|--->| size=100&no_aggs|--->| + cfpbUrl         |--->| complaintId   |
 | searchTerm |    | =true&frm=offset|    | + extractedAt     |    | company       |
 | company    |    |                 |    |                   |    | issue,...     |
 | product    |    +-----------------+    +------------------+    +---------------+
 | state      |
 | dateFrom   |    +-----------------+    +------------------+    +---------------+
 | dateTo     |    | aggregation     |    | aggregations[X]  |    | kind=aggreg.  |
 | sortBy     |--->| mode:           |--->| .buckets ->       |--->| rank, term,   |
 | maxResults |    | size=0&no_aggs= |    | rank+share+count  |    | count, share, |
 | autoPagin. |    | false&field=all |    |                   |    | totalMatching |
 +------------+    +-----------------+    +------------------+    +---------------+

---

Performance & cost

Scenario	Mode	Results	Approx. Run Time	Memory
Quick lookup	records	25	3--5 seconds	256 MB
Default run	records	50	5--10 seconds	256 MB
Medium batch	records	200	15--20 seconds	256 MB
Large batch	records	1,000	30--60 seconds	256 MB
Full single-query	records	10,000	4--6 minutes	256 MB
Top-25 leaderboard	aggregation	25 buckets	2--4 seconds	256 MB
Full distribution	aggregation	up to 10,000 buckets	2--4 seconds	256 MB

Pricing: $0.002 per row emitted (records or aggregation buckets), pay-per-event. A 25-bucket leaderboard run costs ~$0.05; a 10,000-record full-corpus pull costs ~$20. There are no external API costs — the CFPB database is a free US government resource.

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Limitations

• 10,000 records per run (records mode) -- The CFPB API enforces an Elasticsearch single-query limit of 10,000 hits. For full-corpus analysis beyond that, switch to aggregation mode (returns counts over the entire matching set, not capped at 10,000) or split records-mode queries by date range.
• Aggregation mode is per-query -- Each aggregation call returns the buckets for one chosen field across all matching complaints. To group by multiple fields (e.g., company × product), run aggregation once per dimension and join the results downstream.
• No ZIP code filtering -- The CFPB API does not support filtering by ZIP code directly. Filter by state, then post-process using the zipCode field in the output.
• Narrative availability -- The narrative field is only populated when the consumer opted in to public disclosure. Many complaints will have null for this field.
• 15-day publication delay -- Complaints typically appear in the CFPB database approximately 15 days after being sent to the company, to allow time for the company to respond.
• Company name matching -- Company names must match the CFPB's canonical name format (e.g., "EQUIFAX, INC." not just "Equifax"). Partial matches may not return expected results.
• No real-time data -- The database is updated daily by the CFPB, not in real time. There may be a short lag between updates.
• US complaints only -- The CFPB database covers only complaints filed by US consumers against financial companies operating in the United States.

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Responsible use

• Public data only -- This actor accesses a publicly available US government database. All complaint records are published by the CFPB with consumer consent considerations already applied.
• Respectful access -- The actor includes a 200ms delay between API pages and uses no_aggs=true to minimize server load on the government endpoint.
• No personal identification -- While complaints include state and partial ZIP codes, they do not contain personally identifiable information about the consumers who filed them.
• Fair interpretation -- A high volume of complaints does not necessarily indicate wrongdoing. Larger companies naturally receive more complaints. Always normalize by company size or customer base when drawing conclusions.
• Comply with terms -- Users should review the CFPB data terms and ensure their use aligns with applicable regulations and ethical standards.

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FAQ

Do I need a CFPB API key to use this actor? No. The CFPB consumer complaint database is a free, public US government resource. This actor accesses it without any authentication or API key.

How current is the complaint data? The CFPB updates its database daily. Complaints typically appear approximately 15 days after being sent to the company, giving the company time to respond before publication.

Can I get more than 10,000 results per run? The CFPB API enforces a 10,000-record cap on any single query (an Elasticsearch default). To go beyond, you have two options: (1) split records-mode runs by date range and combine the datasets, or (2) use aggregation mode, which returns count buckets computed over the entire matching set without the 10,000-record cap.

What's the difference between records mode and aggregation mode? Records mode returns one row per complaint (the full detail — company, issue, narrative, etc.). Aggregation mode returns top-N count buckets — e.g. "TransUnion: 790,668 complaints, 28.9% of all 2024 complaints." Use records when you need individual complaint details; use aggregation when you need leaderboards, distributions, or share-of-complaints metrics.

What does the autoPaginate flag do? By default the actor stops at maxResults. With autoPaginate=true, when the total matching count exceeds maxResults, the actor extends the run to fetch every matching complaint up to the 10,000 cap. Useful when you want "all matches for this query" without knowing the total in advance.

What does the narrative field contain? When consumers submit complaints, they can include a written description of what happened. If the consumer consents to public disclosure, this text appears in the narrative field. Otherwise it will be null.

Can I search for complaints from a specific ZIP code? The CFPB API does not support direct ZIP code filtering. Filter by state using the state parameter, then filter the output by zipCode in your post-processing.

How far back does the data go? The CFPB complaint database includes records from 2011 to the present, covering over 5 million complaints across all financial product categories.

What product categories are available? The actor supports 10 product filters: Credit reporting/repair, Debt collection, Mortgage, Credit card/prepaid card, Checking/savings account, Student loan, Vehicle loan/lease, Money transfer/virtual currency, Payday/title/personal loan, and Personal loan.

How are company names matched? The company parameter filters by the CFPB's canonical company name. For best results, use the official name as it appears in CFPB records (e.g., "EQUIFAX, INC." rather than "Equifax"). You can also try the searchTerm field for more flexible matching.

Can I schedule this actor to run automatically? Yes. Use Apify's built-in scheduler to run the actor on any cadence -- daily, weekly, or custom cron expressions. Combine with webhooks or integrations to get notified when new data is available.

What does the timely field mean? The timely field indicates whether the company responded to the complaint within the CFPB's expected timeframe (typically 15 days). A value of "Yes" means the response was on time.

Can I export results as CSV or Excel? Yes. Apify datasets support export in JSON, CSV, Excel (XLSX), XML, HTML, and RSS formats. Use the Dataset tab in the console or the Dataset API endpoint with a format parameter.

Is this actor suitable for large-scale research? For datasets up to 10,000 records, a single records-mode run is sufficient. For full-corpus research questions ("what's the complaint distribution across the 2.7 million 2024 complaints?"), use aggregation mode — one call returns counts across the entire matching set. For raw-record needs above 10,000, segment by date range and merge.

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EDGAR Filing Search	Search SEC EDGAR filings by company, form type, or date range. Build a complete regulatory picture alongside CFPB complaint data.
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