CFPB Complaint & Consumer-Finance Risk Monitor

Turn CFPB complaint datasets into company complaint velocity, issue concentration, response-quality and consumer-risk signals. Returns structured, machine-readable results for repeatable research, monitoring, automation, Apify Tasks, API workflows, and AI agents. Returns structured.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "complaintId": "C1",
      "company": "Bank A",
      "product": "Credit card",
      "issue": "Billing dispute"
    },
    {
      "complaintId": "C2",
      "company": "Bank A",
      "product": "Credit card",
      "issue": "Billing dispute"
    },
    {
      "complaintId": "C3",
      "company": "Bank B",
      "product": "Mortgage",
      "issue": "Payment"
    }
  ],
  "previousItems": [
    {
      "complaintId": "P1",
      "company": "Bank A",
      "product": "Credit card",
      "issue": "Fees"
    }
  ],
  "maxItems": 20000
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current records: Current source or normalized rows to analyze.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
• maxItems — Maximum records: Maximum records loaded from a Dataset input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.012000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Detailed documentation

Turn CFPB complaint datasets into company complaint velocity, issue concentration, response-quality and consumer-risk signals.

Why use this Actor

CFPB complaint exports are useful raw data, but compliance and competitive-intelligence teams need recurring company-level risk signals, issue concentration and complaint velocity. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Company complaint-velocity intelligence instead of raw complaint rows.

• Snapshot comparison for emerging issue clusters and worsening firms.

• Issue and product breadth analytics by company.

• Consumer-risk actions for recurring review.

• Low-compute 256 MB data-first architecture.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• Consumer-finance compliance.
• Banking risk.
• Competitive intelligence.
• Reputation monitoring.
• AI agents.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Input

Provide currentItems inline or select currentDatasetId from Apify storage. For period-over-period analysis, add previousItems or previousDatasetId. The Actor uses read-only Dataset permission and accepts up to 50,000 records per run. Stable source identifiers improve exact new/removed record detection, while common aliases are normalized for entity, value, date and secondary dimensions.

Output

The default Dataset receives one compact intelligence report containing current and previous record counts, newly observed and removed records, an aggregate market signal score, ranked entity-level signals, secondary-dimension concentration and a deterministic agentAction. The same report is also saved as INTELLIGENCE_REPORT in key-value storage for downstream Tasks, Schedules, webhooks and agentic workflows.