CFPB Complaint & Consumer-Finance Risk Intelligence

Use this Actor to analyze cfpb complaint and consumer-finance risk and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

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.