Litigation Risk Report

Litigation risk report for any company, generated from 7 federal data sources in under 60 seconds. Built for corporate counsel, compliance teams, institutional investors, and insurance underwriters who need a structured, scored view of a company's legal exposure before making high-stakes decisions.

This actor queries CFPB consumer complaints, SEC EDGAR filings, the Federal Register, Congressional bill data, OFAC sanctions lists, EPA enforcement records, and USPTO patents — all in parallel — then applies four independent scoring models to produce a composite Litigation Risk Score from 0 to 100, with actionable recommendations. No legal database subscriptions required. No manual document review.

What data can you extract?

Why use Litigation Risk Report?

Legal due diligence the traditional way takes days. An associate at a law firm spends 8-12 hours searching PACER, CFPB portals, SEC EDGAR, and EPA databases separately, then synthesizing findings into a memo. Legal data subscription services like Westlaw Edge or LexisNexis charge $500-2,000 per month for access that still requires manual analysis.

This actor automates the entire data collection and initial scoring pipeline. Enter a company name, click Start, and receive a structured report with scores, signals, and action items in under 60 seconds.

• Scheduling — run quarterly monitoring reports on a schedule to track complaint trend shifts over time
• API access — trigger runs from Python, JavaScript, or any HTTP client as part of a larger due diligence workflow
• Proxy rotation — scrape at scale without IP blocks using Apify's built-in proxy infrastructure
• Monitoring — get Slack or email alerts when runs fail or produce unexpected results
• Integrations — connect to Zapier, Make, Google Sheets, HubSpot, or webhooks to route results directly into your existing workflow

Features

• 7 federal data sources queried in parallel — CFPB Consumer Complaints, SEC EDGAR, Federal Register, Congress.gov, OFAC Sanctions, EPA ECHO, and USPTO Patents run simultaneously via Promise.allSettled, keeping total run time under 60 seconds
• Litigation Probability scoring — four sub-components with calibrated weights: CFPB complaint volume (max 35 points), complaint trend acceleration comparing the last 3 months against the prior 3 months (max 15), EPA enforcement actions (max 20), SEC 10-K/10-Q/8-K filing activity (max 15), and disputed complaint responses (max 15)
• Complaint trend analysis — temporal clustering by YYYY-MM buckets; trend ratio computed as recent-quarter average over prior-quarter average; classified as DECLINING, STABLE, RISING, or SURGING (2x+ increase)
• Class Action Warning detection — complaint clustering by issue type and product category; scores issue concentration ratio (largest cluster count / total complaints, max 40), absolute cluster size (max 30), multiple large cluster presence (max 15), and log-scaled total volume (max 15); warning levels: NONE, WATCH, WARNING, IMMINENT
• Enforcement Trajectory tracking — EPA violation count from ECHO status fields, Federal Register rule/enforcement/penalty document filtering, OFAC sanctions matching (each hit scores 15 points, maximum 30), cross-agency amplifier for coordinated multi-agency pressure (max 15)
• Legislative Exposure classification — bills classified into five stages (introduced, committee, passed one chamber, passed both chambers, enacted); enacted bills score 15 points each, proposed Federal Register rules score 5 points each; exposure levels: MINIMAL, MODERATE, SIGNIFICANT, SEVERE
• Weighted composite score — Litigation Probability 30% + Enforcement Trajectory 25% + Class Action Warning 25% + Legislative Exposure 20%; thresholds: 75+ CRITICAL, 50-74 HIGH, 25-49 MODERATE, below 25 LOW
• Automatic action items — five specific trigger conditions generate plain-language recommendations: IMMINENT class action, OFAC matches, SURGING complaint trend, enacted legislation, and 3+ EPA violations
• Industry and jurisdiction context — optional fields refine Federal Register, Congressional, and sanctions queries by appending the company name to sector-specific terms
• Graceful partial failures — if any sub-actor fails, the run continues and scores with available data; failed sources appear as empty arrays, not errors
• Full metadata envelope — every report includes generation timestamp, input parameters, and per-source record counts for audit trail

Use cases for litigation risk reports

Corporate due diligence

M&A counsel and business development teams need to identify legal liabilities before closing an acquisition or forming a joint venture. This actor surfaces CFPB complaint patterns, EPA enforcement history, and OFAC exposure in a single structured report that can be attached to the due diligence file. A target with a SURGING complaint trend or an IMMINENT class action warning score becomes a material negotiation point within minutes, not days.

Compliance monitoring

In-house compliance officers and risk managers can schedule this actor to run quarterly against their own organization — or against key counterparties — to detect shifts in the complaint trend before they become class actions. The temporal clustering model flags a complaint surge in the most recent quarter relative to the prior quarter, giving teams time to investigate root causes and implement remediation before plaintiff firms identify the same pattern.

Institutional investment due diligence

Portfolio managers and analysts evaluating equity or debt positions in public companies need a rapid litigation risk snapshot before and after investment. The SEC EDGAR filing component surfaces 10-K, 10-Q, and 8-K activity that correlates with disclosed litigation risk, while the CFPB component provides consumer-facing signals that may not yet appear in formal disclosures. This actor runs in the time it takes to read a summary memo.

Directors and officers insurance underwriting

Insurance underwriters pricing D&O liability or general commercial liability coverage need to assess a prospective policyholder's enforcement history and legislative exposure. The Enforcement Trajectory model aggregates EPA violations, Federal Register enforcement documents, and OFAC sanctions into a single trajectory score. A CRITICAL trajectory (score 75+) with active OFAC matches is a specific, quantified underwriting signal.

Vendor and counterparty risk screening

Procurement teams and third-party risk managers screening vendors before contract execution need evidence of regulatory standing. A vendor with multiple EPA violations, a rising complaint trend, and significant congressional bill exposure represents a reputational and operational risk. This actor delivers a structured risk profile that can feed directly into vendor risk management platforms via the API.

Law firm business intelligence

Plaintiff-side litigation practices and commercial litigation departments use complaint clustering to identify actionable class-action opportunities and monitor existing class pools. The Class Action Warning model identifies issue concentration (single issue dominating 30%+ of complaints with 10+ complaints) — the same pattern plaintiff firms use to evaluate viability.

How to generate a litigation risk report

1. Enter the company name — Type the company's common name as it would appear in regulatory filings (e.g., "Wells Fargo", "Meta Platforms", "Exxon Mobil"). Full legal names work better than ticker symbols.
2. Add optional context — Enter the industry (e.g., "banking", "pharmaceuticals", "energy") to refine Federal Register and Congressional bill queries. Add a jurisdiction (e.g., "United States") to focus sanctions screening.
3. Click Start and wait — The actor queries all 7 federal databases in parallel. Most runs complete in 30-60 seconds. Complex company names with many regulatory filings may take up to 90 seconds.
4. Download the report — Open the Dataset tab and export as JSON, CSV, or Excel. The full structured report is in one record per run. The composite score, risk level, all signals, and action items are top-level fields for easy filtering.

Input parameters

Input examples

Standard company analysis — most common use case:

{
  "companyName": "Wells Fargo",
  "industry": "banking",
  "jurisdiction": "United States"
}

Pharmaceutical company with regulatory focus:

{
  "companyName": "Johnson & Johnson",
  "industry": "pharmaceuticals",
  "jurisdiction": "United States"
}

Minimal run — name only, fastest execution:

{
  "companyName": "Exxon Mobil"
}

Input tips

• Use the regulatory name — "Wells Fargo Bank N.A." will match more CFPB and EDGAR records than just "Wells Fargo". Try both and compare complaint counts.
• Add industry for better legislative coverage — The Congressional bill query uses companyName + industry as the search term, which surfaces sector-specific legislation more accurately.
• Jurisdiction matters for OFAC — The default query is the company name alone. Adding "United States" or a specific country filters sanctions results to entities with that geographic context.
• Run without industry first — For a broad baseline, omit industry and jurisdiction. Add context in a second run to see how legislative and sanctions scores shift.
• Quarterly scheduling — Set a scheduled run every 90 days to track complaint trend changes. The SURGING trigger fires when the most recent 3-month average is 2x or more above the prior 3 months.

Output example

{
  "company": "Pinnacle Financial Services",
  "compositeScore": 62,
  "riskLevel": "HIGH",
  "litigationProbability": {
    "score": 71,
    "complaintCount": 48,
    "complaintTrend": "RISING",
    "enforcementActions": 4,
    "edgarFilings": 12,
    "riskLevel": "HIGH",
    "signals": [
      "48 CFPB complaints — elevated litigation risk",
      "4 EPA violations/enforcement actions",
      "7 disputed/untimely complaint responses"
    ]
  },
  "classActionWarning": {
    "score": 58,
    "clusterCount": 3,
    "largestCluster": {
      "issue": "Managing an account",
      "count": 19
    },
    "productClusters": [
      { "product": "Checking or savings account", "count": 27 },
      { "product": "Credit card", "count": 14 },
      { "product": "Mortgage", "count": 7 }
    ],
    "issueClusters": [
      { "issue": "Managing an account", "count": 19 },
      { "issue": "Fees or interest", "count": 11 },
      { "issue": "Incorrect information on your report", "count": 8 }
    ],
    "warningLevel": "WARNING",
    "signals": [
      "\"Managing an account\" — 19 complaints (40% concentration)",
      "3 distinct issue clusters with 5+ complaints each"
    ]
  },
  "enforcementTrajectory": {
    "score": 55,
    "epaViolations": 4,
    "federalRegisterActions": 6,
    "sanctionsExposure": 0,
    "trajectoryDirection": "ESCALATING",
    "agencies": ["EPA", "Federal Register"],
    "signals": [
      "4 EPA compliance violations",
      "6 relevant Federal Register enforcement/rule documents"
    ]
  },
  "legislativeExposure": {
    "score": 45,
    "relevantBills": 14,
    "billsByStage": {
      "introduced": 8,
      "committee": 4,
      "passed_one": 1,
      "passed_both": 0,
      "enacted": 1
    },
    "highImpactBills": 2,
    "exposureLevel": "MODERATE",
    "signals": [
      "1 enacted bill(s) creating new compliance obligations",
      "14 relevant bills in Congress — significant legislative attention"
    ]
  },
  "allSignals": [
    "48 CFPB complaints — elevated litigation risk",
    "4 EPA violations/enforcement actions",
    "7 disputed/untimely complaint responses",
    "\"Managing an account\" — 19 complaints (40% concentration)",
    "3 distinct issue clusters with 5+ complaints each",
    "4 EPA compliance violations",
    "6 relevant Federal Register enforcement/rule documents",
    "1 enacted bill(s) creating new compliance obligations",
    "14 relevant bills in Congress — significant legislative attention"
  ],
  "actionItems": [
    "Complaint volume rising — monitor trend for surging threshold",
    "Multiple EPA violations — environmental remediation and compliance audit recommended",
    "New legislation enacted — review compliance program for gaps"
  ],
  "metadata": {
    "generatedAt": "2026-03-20T14:22:11.000Z",
    "industry": "financial services",
    "jurisdiction": "United States",
    "dataSources": {
      "cfpbComplaints": 48,
      "edgarFilings": 12,
      "federalRegister": 18,
      "congressBills": 14,
      "ofacSanctions": 0,
      "epaRecords": 9,
      "patents": 6
    }
  }
}

Output fields

How much does it cost to generate a litigation risk report?

Litigation Risk Report uses pay-per-run pricing — you pay approximately $0.15 per report. Platform compute costs are included. The actor calls 7 sub-actors in parallel; the total cost reflects their combined compute time.

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached.

Compare this to Westlaw Edge at $300-600/month or LexisNexis at $500-2,000/month — with Litigation Risk Report, most due diligence teams spend $1.50-15.00 per project with no subscription commitment and no per-seat licensing.

Litigation risk reports using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/litigation-risk-report").call(run_input={
    "companyName": "Pinnacle Financial Services",
    "industry": "financial services",
    "jurisdiction": "United States"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Company: {item['company']}")
    print(f"Composite Score: {item['compositeScore']}/100 ({item['riskLevel']})")
    print(f"Class Action Warning: {item['classActionWarning']['warningLevel']}")
    print(f"Enforcement Trajectory: {item['enforcementTrajectory']['trajectoryDirection']}")
    print(f"Action Items: {item['actionItems']}")

JavaScript

import { ApifyClient } from "apify-client";

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

const run = await client.actor("ryanclinton/litigation-risk-report").call({
    companyName: "Pinnacle Financial Services",
    industry: "financial services",
    jurisdiction: "United States"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    console.log(`Company: ${item.company}`);
    console.log(`Score: ${item.compositeScore}/100 — ${item.riskLevel}`);
    console.log(`Signals: ${item.allSignals.length} detected`);
    console.log(`Action items: ${item.actionItems.join("; ")}`);
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~litigation-risk-report/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"companyName": "Pinnacle Financial Services", "industry": "financial services", "jurisdiction": "United States"}'

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

How Litigation Risk Report works

Phase 1: Parallel data collection across 7 federal sources

The actor constructs up to three distinct query terms from the inputs: the raw company name (used for CFPB, EDGAR, OFAC, EPA, and patent queries), the company name plus industry (used for Federal Register and Congress queries), and the company name plus jurisdiction (used for OFAC). All seven sub-actors are called simultaneously using Promise.allSettled, so the total run time equals the slowest individual sub-actor rather than the sum of all seven. If any sub-actor fails — due to a timeout, API error, or no results — that source is recorded as an empty array and the run continues with the remaining data.

Each sub-actor is allocated 256 MB of memory and a 120-second timeout. Results are limited to 1,000 items per source to cap costs. The seven sources are: CFPB Consumer Complaint Database (federal consumer financial complaints), SEC EDGAR full-text search (public company filings), Federal Register (federal agency rules and notices), Congress.gov bill search (legislative activity), OFAC Consolidated Sanctions List (sanctions and SDN matches), EPA ECHO (environmental compliance and enforcement), and USPTO patent database (patent activity for IP litigation signals).

Phase 2: Four independent scoring models

Litigation Probability processes CFPB complaints in two passes. The first pass calculates raw volume score (complaints × 1.5, capped at 35). The second pass groups complaints into YYYY-MM buckets, computes rolling 3-month averages for the two most recent windows, and calculates a trend ratio. A ratio of 2.0+ triggers SURGING status and a time-bonus score. EPA enforcement records are filtered by status fields containing VIOLATION, ENFORCEMENT, or NON-COMPLIANCE. EDGAR records are filtered to 10-K, 10-Q, and 8-K form types. Disputed complaints are identified by company_response fields containing "dispute" or "untimely".

Class Action Warning runs a single-pass cluster analysis over all CFPB complaints, building two frequency maps: one keyed on the issue field and one on the product field. The top 10 entries from each map are returned as sorted arrays. The concentration score uses the ratio of the largest cluster to total complaints — a ratio above 0.30 with at least 10 complaints in that cluster is the primary class-action signal. The log-scaled volume amplifier (log2(complaints) × 3) ensures large absolute volumes contribute meaningfully even when no single issue dominates.

Enforcement Trajectory queries three sources. EPA records are filtered identically to the Litigation Probability model. Federal Register documents are filtered on document type (proposed rule, final rule) and title keywords (enforcement, penalty, violation). OFAC records receive the heaviest weighting — each match scores 15 points up to a maximum of 30 — because any OFAC match represents severe legal exposure requiring immediate action. A cross-agency amplifier adds 5 points per agency with active signals, up to 15, capturing the compound risk of coordinated multi-agency pressure.

Legislative Exposure classifies each Congressional bill into one of five stages by searching the status, latestAction, or actionDesc fields for keywords: enacted/became law/signed, passed senate/passed house, committee/referred, or default (introduced). Federal Register documents are filtered to proposed and final rules as a companion signal. Enacted bills score 15 points each, reflecting the immediate compliance obligation they create.

Phase 3: Composite scoring and action item generation

The composite score is a fixed-weight average: (litigation × 0.30) + (enforcement × 0.25) + (classAction × 0.25) + (legislative × 0.20). Five specific high-severity conditions generate automatic action items in plain language: IMMINENT class action warning (score 75+), any OFAC match, SURGING complaint trend, any enacted legislation, and 3 or more EPA violations. The complete report is pushed to the actor's dataset as a single JSON record alongside a metadata envelope containing the generation timestamp and per-source record counts.

Tips for best results

1. Match the regulatory name exactly. CFPB and EPA records use legal entity names. "JPMorgan Chase Bank, N.A." will return more CFPB records than "JPMorgan". If you get a low complaint count for a large financial institution, try the full registered name.

2. Run parent company and subsidiaries separately. CFPB complaints are often filed against specific subsidiaries, not the parent holding company. Run both "Wells Fargo & Company" and "Wells Fargo Bank, N.A." to capture the full picture, then compare composite scores.

3. Use industry for legislative precision. Without an industry term, the Congressional bill query searches only the company name, which may miss sector-wide bills that don't mention the company. Adding "banking" or "pharmaceutical" significantly improves legislative exposure coverage.

4. Schedule quarterly runs and compare scores. A single point-in-time score is useful. A trend of scores — LOW in Q1, MODERATE in Q2, HIGH in Q3 — is the early warning that litigation is building. Use Apify's scheduler to automate this.

5. Combine with Company Deep Research for a complete picture. This actor provides quantitative litigation scores; Company Deep Research adds qualitative context from news, analyst reports, and public records.

6. Check the dataSources metadata field. If cfpbComplaints is 0 for a large consumer-facing company, the company name may not match the CFPB database. Try alternate spellings. If ofacSanctions is non-zero, treat this as a critical finding requiring immediate legal review regardless of the composite score.

7. Low scores are not clearance. A LOW composite score means no detectable signals in these seven federal databases. It does not mean the company is free of litigation. Private litigation, state-level enforcement, and foreign regulatory actions are outside the scope of this actor.

Combine with other Apify actors

Limitations

• US federal sources only. All seven data sources are US government databases. The actor does not query state courts, private litigation databases (PACER, CourtListener), foreign regulatory bodies, or private settlement records. A company with significant state-level litigation but no federal footprint may score LOW incorrectly.
• No real-time court dockets. Active lawsuits filed in federal or state courts are not directly queried. The CFPB complaint data and EPA enforcement records are proxies for litigation risk, not direct lawsuit records.
• Company name matching is lexical. Sub-actors query by the string provided. Common names (e.g., "First National Bank") may return results for multiple unrelated entities. Uncommon names or recent rebrands may miss historical records filed under a prior name.
• CFPB data covers financial services companies. Companies outside consumer financial services (fintech, banking, lending, insurance, debt collection) will have low or zero CFPB records. The complaint-dependent scoring models (Litigation Probability, Class Action Warning) will produce lower scores for non-financial companies regardless of actual risk.
• EPA data is relevant to manufacturing, energy, and chemicals. Service companies, software firms, and technology companies with no physical facilities will typically score near zero on the Enforcement Trajectory EPA component.
• Legislative exposure is keyword-based. The Congressional bill search uses the company name plus industry as a text query. Bills that affect an industry broadly without naming a specific company may be missed. Bills that mention a company name in context other than direct regulation may inflate the score.
• OFAC false positives are possible. The OFAC query uses the company name as a text search. Common words in a company name may match partial OFAC entries. Treat OFAC matches as signals requiring human review, not automatic disqualification.
• Data freshness depends on sub-actor update cycles. CFPB data is updated weekly. OFAC lists are updated on business days. Congressional and Federal Register data updates within 24-48 hours of publication. EPA records may lag by weeks. Scores reflect data available at run time.
• 1,000 record limit per source. Each sub-actor retrieves up to 1,000 records. Companies with extremely high complaint volumes (national banks, major insurers) may exceed this limit, causing scores to be underestimated. Consider this when interpreting scores for companies with known mass-complaint histories.

Integrations

• Zapier — trigger a litigation risk report automatically when a new company is added to a CRM or spreadsheet, and route results to a Slack channel
• Make — build multi-step workflows: run the report, check if riskLevel is HIGH or CRITICAL, and send an email alert with the action items
• Google Sheets — export composite scores and risk levels for a portfolio of companies into a watchlist spreadsheet with automatic updates
• Apify API — integrate litigation screening into due diligence pipelines, vendor onboarding workflows, or investment decision systems
• Webhooks — push completed report data to any internal system when a run finishes, with no polling required
• LangChain / LlamaIndex — feed litigation risk reports as structured context into LLM-based legal research or investment analysis pipelines

Troubleshooting

• Composite score seems too low for a well-known company — The actor queries by exact company name string. A company like "Citigroup" may have most CFPB complaints filed under "Citibank, N.A." or "Citi" — try alternate names. Also check the dataSources metadata: if cfpbComplaints is 0 or very low for a large consumer lender, the name did not match.

• Run completes but all sub-scores are 0 — This typically means no records were returned from any source. Verify the company name is spelled correctly and is a real entity with US federal exposure. Very small private companies, international companies with no US operations, and recently formed entities may have no federal records. Adding an industry term can improve Congressional and Federal Register matching.

• OFAC sanctions exposure is non-zero but the company seems legitimate — OFAC text search can return partial matches on common words. For example, a company named "Iran Consulting Group" would match OFAC entries related to Iran. Review the raw OFAC source data to confirm whether matches are the actual entity or lexical coincidences. Use the WHOIS Domain Lookup actor to verify the company's actual registration jurisdiction.

• Run timing out — The actor allocates 120 seconds per sub-actor. In rare cases, one or more federal databases may be slow. The Promise.allSettled pattern means slow sub-actors don't block others, but if the overall run times out before all sub-actors complete, the timed-out sources will score as empty. Try re-running — most timeouts are transient.

• Getting identical scores across different companies — This can happen if the company name is too generic and all queries return the same results. Use the most specific legal name available. Adding industry and jurisdiction inputs differentiates the Federal Register and OFAC queries.

Responsible use

• This actor only accesses publicly available federal government data. All data sources are open-access government databases with no authentication requirements.
• Respect the terms of service of CFPB, SEC EDGAR, Federal Register, Congress.gov, OFAC, EPA, and USPTO.
• Scores produced by this actor are analytical signals derived from public records. They are not legal opinions, legal advice, or determinations of liability.
• Comply with FCRA, GDPR, and other applicable regulations when using litigation risk scores in employment screening, credit decisions, or insurance pricing contexts. Federal law restricts using certain types of public records in specific decision-making contexts.
• Do not use scores to defame, harass, or make unsubstantiated public claims about a company's legal standing.
• For guidance on web scraping and data use legality, see Apify's guide.

FAQ

How is the litigation risk composite score calculated? The composite score is a weighted average of four sub-model scores: Litigation Probability (30%), Enforcement Trajectory (25%), Class Action Warning (25%), and Legislative Exposure (20%). Each sub-score is independently capped at 100 before weighting. The composite thresholds are: below 25 = LOW, 25-49 = MODERATE, 50-74 = HIGH, 75+ = CRITICAL.

What federal data sources does the litigation risk report query? The actor queries seven sources simultaneously: CFPB Consumer Complaint Database, SEC EDGAR full-text filings, Federal Register rules and notices, Congress.gov bill search, OFAC Consolidated Sanctions List, EPA ECHO environmental compliance records, and USPTO patent database. All seven run in parallel via Promise.allSettled.

How long does a typical litigation risk report run take? Most runs complete in 30-60 seconds because all seven sub-actors run in parallel. The total time equals the slowest individual sub-actor, not the sum. Complex company names with many matching records may take up to 90 seconds.

What triggers an "IMMINENT" class action warning? A Class Action Warning score of 75 or higher. This requires high issue concentration — a single issue type representing 30%+ of all complaints with at least 10 complaints on that issue — plus significant absolute cluster size and total complaint volume. "IMMINENT" does not mean a class action has been filed; it means the complaint pattern closely resembles patterns that have historically preceded class action filings.

Can I use this litigation risk report for international companies? The data sources are US federal databases. International companies with US operations, SEC filings, US consumer products, or OFAC exposure will produce meaningful results. Companies with no US footprint will return near-zero scores across all models, which reflects the absence of US-observable signals — not necessarily an absence of litigation risk globally.

How is this different from Westlaw or LexisNexis litigation research? Westlaw and LexisNexis provide access to case law, dockets, and attorney-written research. This actor provides a structured quantitative score derived from regulatory and enforcement data — no legal interpretation required. It costs $0.15 per run versus hundreds per month for legal database subscriptions. It is a screening tool, not a replacement for full legal research on matters that reach the action stage.

How often should I run a litigation risk report on the same company? For active monitoring, quarterly runs capture complaint trend shifts, new enforcement actions, and legislative changes. For one-time due diligence, a single run provides a point-in-time snapshot. The complaint trend model specifically compares the last 3 months against the prior 3 months, so quarterly cadence aligns with the model's natural detection window.

Is it legal to use CFPB, EPA, and OFAC data for business screening? Yes. All seven sources are public government databases designed for public access. CFPB makes complaint data publicly available to promote transparency. EPA ECHO is a public enforcement tracking system. OFAC publishes its sanctions lists specifically for compliance screening purposes. The data itself is public; your obligations relate to how you use it (see FCRA for credit and employment contexts).

What does a score of 0 mean for a large company? A score near zero typically means the company name did not match records in these federal databases — often because the legal entity name differs from the common name, the company has no US operations, or it operates in sectors not covered by CFPB (e.g., pure B2B software). Check the metadata.dataSources object: if all source counts are 0, the query did not match. Try the full registered legal name.

Can I screen multiple companies in one run? No. Each run processes one company. To screen a list of companies, trigger separate runs via the API or use Apify's scheduler to queue them. The API example in this README shows a simple loop pattern for batch screening in Python or JavaScript.

What does "SURGING" complaint trend mean and why does it matter? SURGING means the average monthly complaint count in the most recent 3-month window is 2x or more than the average in the prior 3-month window. Plaintiff firms actively monitor the CFPB database for exactly this pattern — a rapid acceleration in complaints about a specific issue is a strong predictor of class action formation within 6-18 months.

Can I schedule this actor to monitor a list of companies automatically? Yes. Apify's scheduler supports cron-based scheduling. Set up individual scheduled runs for each company you want to monitor quarterly. Use webhooks or the Zapier integration to route completed reports to a shared spreadsheet or Slack channel automatically.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.