Regulatory Change Tracker

Regulatory change tracking across 13 US federal data sources in a single parallel run — built for compliance officers, legal teams, trade policy analysts, and government affairs professionals who need structured, machine-readable regulatory intelligence at scale. Enter a keyword, get back a composite Compliance Impact Score (0-100), four scored sub-models, and an actionable recommendation in 2-4 minutes.

Unlike manual monitoring across individual agency websites, this actor aggregates the Federal Register, Congressional bills, OSHA inspections, EPA enforcement, DOL Wage and Hour, FDA recalls, CBP customs rulings, USITC tariffs, SEC EDGAR, Senate lobbying disclosures, FEC contributions, and regulatory website changes simultaneously. All 13 sources run in parallel and the findings are synthesized into four quantified scoring models with a weighted composite grade — ready for dashboards, GRC platforms, or automated alerts.

What data can you extract?

Why use Regulatory Change Tracker?

Tracking regulatory changes manually means visiting the Federal Register daily, checking OSHA inspection data, monitoring CBP rulings, reading Congressional bill status, and synthesizing lobbying disclosures — across 13 separate government websites. A compliance analyst doing this for two or three regulatory topics spends 3-4 hours per week per topic and still produces no quantified risk score. There is no single government source that shows you when enforcement is intensifying, legislation is advancing, and tariff rulings are shifting at the same time.

This actor automates the entire process. One API call, one structured output record, four scored models, a composite grade, and an actionable recommendation. Run it on a schedule to detect regulatory shifts as they emerge — not after they become compliance obligations.

Platform benefits that matter for compliance and legal workflows:

• Scheduling — run weekly or monthly to track how the Compliance Impact Score evolves and detect acceleration before it becomes a crisis
• API access — trigger runs from Python, JavaScript, or any HTTP client to feed compliance dashboards and GRC platforms
• Proxy rotation — all 13 sub-actors execute with Apify's built-in infrastructure so no government API gets rate-limited
• Monitoring — get Slack or email alerts when a run's score exceeds a threshold you define
• Integrations — push output to Google Sheets, HubSpot, Zapier, or webhooks to route high-risk findings into triage workflows

Features

• 13 federal data sources queried in parallel — Federal Register, Congress bills, CBP customs rulings, USITC tariff schedule, SEC EDGAR, OSHA inspections, EPA ECHO enforcement, DOL Wage and Hour enforcement, FDA food recalls, FDA device recalls, Senate lobbying disclosures, FEC campaign finance, and regulatory website change monitoring
• Legislative Probability Engine (0-100) — classifies each bill by parsing status and latestAction fields for stage keywords: enacted/signed/became law (+35), passed/agreed to (+25), committee/referred/reported (+8-15), introduced (+3-8); amplified by Federal Register rulemaking volume (+5-12) and lobbying count above 10 (+5); graded NEAR CERTAIN / LIKELY / POSSIBLE / UNLIKELY
• Enforcement Trend Detector (0-100) — scores six agencies independently: OSHA inspections up to +20, EPA ECHO records up to +20, DOL WHD actions up to +15, FDA recalls plus devices combined up to +15, SEC filings flat +5; Federal Register documents matching "enforcement", "penalty", "violation", or "compliance" add +10; graded INTENSIFYING / ACTIVE / MODERATE / DORMANT
• Tariff Impact Analyzer (0-100) — scores CBP customs rulings (up to +30), USITC HTS tariff entries (up to +25), trade-related Federal Register notices filtered by "tariff", "trade", "import", "export", "customs", "duty" (up to +20), trade bills in Congress (+15), and lobbying records matching trade keywords (+10); graded HIGH DISRUPTION / MODERATE / LOW / MINIMAL IMPACT
• Regulatory Domino Effect (-100 to +100) — assigns per-agency threshold scores (OSHA >3: +15, EPA >3: +15, DOL >3: +15, FDA combined >3: +15, SEC >5: +10, CBP >3: +10), adds a +20 cascade bonus when 4+ agencies are simultaneously active, and adds Federal Register volume (+10) and Congressional bill count (+10) bonuses; normalized to 0-100 for composite weighting; direction labeled CASCADING EXPANSION / EXPANDING / STABLE / CONTRACTING
• Composite Compliance Impact Score — weighted average: Legislative Probability 30% + Enforcement Trend 30% + Domino Effect 25% + Tariff Impact 15%; graded CRITICAL ≥75 / HIGH ≥50 / MODERATE ≥25 / LOW <25
• Bill stage tracking — counts bills at each legislative stage and includes titles, bill numbers, sponsor names, and latest action status for the top 15 results
• Lobbying aggregation — groups Senate lobbying filings by registrant firm and returns the top 15 firms by filing count
• FEC contribution mapping — aggregates political contributions by recipient committee or candidate and returns the top 15 recipients
• Federal Register categorization — counts proposed rules, final rules, notices, and other document types separately
• Sub-actor resilience — each of the 13 source actors runs in an isolated error handler; if one source fails or times out, the remaining 12 still complete and the report is generated with available data
• Jurisdiction and date range filtering — append a jurisdiction string (e.g., "California", "FDA") to narrow all 13 queries simultaneously

Use cases for regulatory change tracking

Compliance program monitoring

Compliance officers at regulated businesses — financial services, healthcare, manufacturing, food and beverage — use this actor to generate a weekly regulatory pulse report for their sector. The Compliance Impact Score provides a single number to report to the board. The individual model scores tell the team where to focus: if the Legislative Probability score jumps from 30 to 65 over two weeks, a proposed rule is moving toward enactment and the compliance program needs updating before enforcement begins.

Trade and supply chain risk assessment

Trade compliance teams and import/export managers run this actor against product categories, HTS codes, or industry keywords to monitor tariff and customs ruling changes. The Tariff Impact Analyzer surfaces CBP binding rulings and USITC tariff schedule changes that affect landed costs. A score above 40 (MODERATE DISRUPTION) is a trigger to brief procurement teams and re-evaluate supplier contracts or duty drawback strategies.

Government affairs and policy intelligence

Government affairs directors and industry association staff use the actor to track lobbying density and FEC contribution patterns around regulatory issues. The top lobbying firms and FEC recipients fields reveal who is actively shaping regulatory outcomes and which political committees are receiving industry money — intelligence that informs coalition strategy, comment letter timing, and regulatory engagement calendars.

M&A regulatory due diligence

Investment banks and private equity legal teams use this actor during due diligence to quantify regulatory risk for acquisition targets. Running the actor against a target company's regulatory exposure areas — industry sector, product categories, geographic markets — produces a structured risk snapshot that can be compared across multiple targets. A CRITICAL COMPLIANCE RISK grade on a pending acquisition is a material finding that affects valuation or deal terms.

Enterprise legal and general counsel briefings

General counsel offices and outside law firms use this actor to produce structured regulatory briefings for clients. Running the actor weekly against 10-20 client-specific regulatory topics and routing high-score results through a webhook into a matter management system replaces hours of associate research time. The recommendation field provides a plain-language advisory sentence that can be included directly in client communications.

Hedge fund regulatory event research

Systematic macro and event-driven funds use the Legislative Probability scores to quantify the likelihood that pending legislation passes and affects specific sectors. A bill moving from POSSIBLE to LIKELY in the Legislative Probability Engine is a quantified signal about sector rotation risk that feeds pre-trade analysis workflows.

How to track regulatory changes

1. Enter your regulatory query — type an industry, regulation name, or policy keyword into the Query field. Examples: "PFAS chemicals", "AI safety regulation", "Section 232 steel tariffs", "pharmacy benefit managers". Specific terms return more targeted data than broad sector names.
2. Add a jurisdiction if needed — leave blank for full US federal coverage, or enter "California", "FDA", "SEC", or "EPA" to narrow all 13 source queries simultaneously.
3. Click Start and wait 2-4 minutes — all 13 sub-actors run in parallel with 120-second timeouts each. Total runtime depends on data volume for your topic.
4. Download your report — one structured JSON record appears in the Dataset tab. Export to JSON, CSV, or Excel. The Compliance Impact Score, grade, and recommendation are at the top level for immediate scanning.

Input parameters

Input examples

Standard industry regulatory scan:

{
  "query": "PFAS chemicals",
  "jurisdiction": "EPA"
}

Trade and tariff exposure check:

{
  "query": "Section 232 steel tariffs",
  "jurisdiction": "CBP",
  "dateRange": "2025"
}

Minimal quick scan with defaults:

{
  "query": "AI safety regulation"
}

Input tips

• Be specific in your query — "pharmacy benefit manager regulation" returns more targeted Federal Register and Congressional data than "healthcare". Specificity narrows all 13 sources simultaneously.
• Use agency names as jurisdiction filters — entering "FDA" or "SEC" focuses all 13 queries on agency-specific terminology and improves result relevance from enforcement sources.
• Run the same query weekly to detect score movement — a Compliance Impact Score rising 15+ points over 30 days signals an accelerating regulatory environment and warrants immediate review.
• Use multiple focused runs instead of one broad query — if you track both "PFAS" and "Section 232 steel", run two separate queries. Combined queries dilute results across all 13 sources.
• Combine with scheduling — set a weekly schedule on Apify and route score increases above 50 through a webhook to Slack or your GRC platform for automatic triage.

Output example

{
  "query": "PFAS chemicals",
  "jurisdiction": "EPA",
  "dateRange": "Current",
  "generatedAt": "2026-03-20T11:42:17.000Z",
  "complianceImpactScore": 74,
  "complianceGrade": "CRITICAL COMPLIANCE RISK",
  "recommendation": "Significant regulatory change underway. Immediate compliance review required across multiple agencies.",
  "models": {
    "legislativeProbability": {
      "score": 78,
      "label": "NEAR CERTAIN",
      "findings": [
        "1 enacted bill(s) — regulatory change is LIVE",
        "2 bill(s) passed at least one chamber — high probability of enactment",
        "7 bills in committee — significant legislative attention",
        "12 proposed/final rules in Federal Register — active rulemaking",
        "14 lobbying filings — high industry engagement"
      ],
      "billStages": {
        "introduced": 9,
        "committee": 7,
        "passed": 2,
        "enacted": 1
      }
    },
    "enforcementTrend": {
      "score": 67,
      "direction": "INTENSIFYING",
      "findings": [
        "24 EPA enforcement records — heavy environmental scrutiny",
        "11 DOL WHD enforcement actions — labor compliance crackdown",
        "4 enforcement-related Federal Register notice(s)",
        "4 agencies actively enforcing — multi-front regulatory pressure"
      ],
      "agencyActivity": {
        "EPA": 24,
        "DOL-WHD": 11,
        "OSHA": 4,
        "FDA": 3
      }
    },
    "tariffImpact": {
      "score": 28,
      "label": "LOW DISRUPTION",
      "findings": [
        "2 CBP customs ruling(s)",
        "3 trade-related Federal Register notice(s)"
      ]
    },
    "dominoEffect": {
      "score": 75,
      "direction": "CASCADING EXPANSION",
      "findings": [
        "4 agencies actively enforcing — HIGH domino cascade risk",
        "Active agencies: EPA, DOL-WHD, OSHA, FDA",
        "22 Federal Register entries — regulatory momentum building",
        "9 congressional bills — legislative pressure amplifies regulatory cascade"
      ],
      "activeAgencies": ["EPA", "DOL-WHD", "OSHA", "FDA"]
    }
  },
  "dataSources": {
    "federalRegister": {
      "total": 22,
      "breakdown": { "proposed": 8, "final": 5, "notice": 6, "other": 3 },
      "entries": [
        {
          "title": "PFAS National Primary Drinking Water Regulation",
          "type": "Final Rule",
          "agency": "Environmental Protection Agency",
          "publishDate": "2026-02-14"
        }
      ]
    },
    "congressBills": {
      "total": 19,
      "bills": [
        {
          "title": "PFAS Action Act of 2025",
          "number": "S.1732",
          "status": "Committee on Environment and Public Works",
          "sponsor": "Sen. Gillibrand, Kirsten"
        }
      ]
    },
    "lobbying": {
      "totalFilings": 14,
      "topFirms": [
        { "firm": "American Chemistry Council", "filings": 6 },
        { "firm": "Dentons US LLP", "filings": 3 }
      ]
    },
    "politicalFinance": {
      "totalContributions": 11,
      "topRecipients": [
        { "recipient": "Senate Environment and Public Works PAC", "contributions": 4 }
      ]
    },
    "tradeData": {
      "cbpCustomsRulings": 2,
      "usitcTariffEntries": 3
    },
    "enforcement": {
      "oshaInspections": 4,
      "epaEcho": 24,
      "dolWhd": 11,
      "fdaRecalls": 3,
      "fdaDevices": 0,
      "secEdgar": 2
    },
    "websiteChanges": 1
  }
}

Output fields

How much does it cost to track regulatory changes?

Regulatory Change Tracker uses pay-per-run pricing — each run queries 13 federal data sources in parallel and costs approximately $0.50-$1.20 depending on data volume for your regulatory topic. Platform compute costs are included.

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

Commercial regulatory intelligence platforms charge $500-2,000/month per user for comparable multi-source monitoring (Compliance.ai, LexisNexis Regulatory Compliance, RegScan). With this actor, most compliance teams monitoring 10-20 topics spend under $30/month with no subscription commitment.

Track regulatory changes using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/regulatory-change-tracker").call(run_input={
    "query": "PFAS chemicals",
    "jurisdiction": "EPA"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    score = item["complianceImpactScore"]
    grade = item["complianceGrade"]
    recommendation = item["recommendation"]
    leg_label = item["models"]["legislativeProbability"]["label"]
    enf_direction = item["models"]["enforcementTrend"]["direction"]
    active_agencies = item["models"]["dominoEffect"]["activeAgencies"]

    print(f"Compliance Impact: {score}/100 — {grade}")
    print(f"Recommendation: {recommendation}")
    print(f"Legislative Probability: {leg_label}")
    print(f"Enforcement Trend: {enf_direction}")
    print(f"Active agencies: {', '.join(active_agencies)}")

JavaScript

import { ApifyClient } from "apify-client";

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

const run = await client.actor("ryanclinton/regulatory-change-tracker").call({
    query: "PFAS chemicals",
    jurisdiction: "EPA"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    const { complianceImpactScore, complianceGrade, recommendation, models } = item;
    console.log(`Score: ${complianceImpactScore}/100 — ${complianceGrade}`);
    console.log(`Recommendation: ${recommendation}`);
    console.log(`Legislative: ${models.legislativeProbability.label}`);
    console.log(`Enforcement: ${models.enforcementTrend.direction}`);
    console.log(`Active agencies: ${models.dominoEffect.activeAgencies.join(", ")}`);
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~regulatory-change-tracker/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "PFAS chemicals", "jurisdiction": "EPA"}'

# 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 Regulatory Change Tracker works

Phase 1: Parallel data collection across 13 federal sources

When the actor starts, it builds a combined search string from the query and optional jurisdiction fields (e.g., "PFAS chemicals EPA"). It then fires 13 sub-actor calls simultaneously via Promise.all. Each sub-actor runs with a 120-second timeout and 256 MB memory allocation. The sub-actors are purpose-built federal data wrappers from the ryanclinton catalogue:

• Legislative: federal-register-search (30 results), congress-bill-search (20 results)
• Political influence: senate-lobbying-search (30 results), fec-campaign-finance (20 results)
• Trade/tariff: cbp-customs-rulings (20 results), usitc-hts-tariff-search (30 results)
• Financial regulatory: edgar-filing-search (15 results)
• Workplace/safety enforcement: osha-inspection-search (20 results), epa-echo-search (20 results), dol-whd-enforcement (15 results)
• Product safety: fda-food-recall-monitor (15 results), fda-device-recalls (15 results)
• Monitoring: website-change-monitor (10 results)

Each sub-actor call is wrapped in an isolated try/catch. If one source fails or times out, the remaining 12 complete and the scoring models run on available data — the report is always generated.

Phase 2: Four-model scoring pipeline

Once all 13 sources return data, four scoring functions execute synchronously against the aggregated dataset.

The Legislative Probability Engine classifies each bill by parsing status and latestAction field text for stage keywords: "enacted"/"signed"/"became law" scores 35 points, "passed"/"agreed to" scores 25 points, "committee"/"referred"/"reported" scores 8-15 points, and default-introduced bills score 3-8 points. Federal Register proposed and final rules add 5-12 points based on volume. Lobbying file count above 10 adds a further 5 points.

The Enforcement Trend Detector scores each of six agencies independently. OSHA and EPA can each contribute up to 20 points at record counts above 20; DOL-WHD contributes up to 15 points at counts above 10; FDA recalls and devices combined contribute up to 15 points; SEC filings add a flat 5 points. Federal Register documents matching "enforcement", "penalty", "violation", or "compliance" in their title or abstract add 10 additional points.

The Tariff Impact Analyzer scores CBP customs rulings up to 30 points, USITC HTS tariff entries up to 25 points, Federal Register documents matched against six trade keywords up to 20 points, trade-related Congress bills 15 points, and lobbying records mentioning trade keywords 10 points.

The Regulatory Domino Effect assigns per-agency threshold scores (OSHA >3 inspections: +15, EPA >3 records: +15, DOL >3 actions: +15, FDA combined >3: +15, SEC >5: +10, CBP >3: +10) then adds a +20 cascade bonus when 4+ agencies simultaneously exceed their thresholds. Federal Register volume above 15 adds 10 points; Congressional bills above 5 add another 10 points. The -100 to +100 range is normalized to 0-100 for the composite calculation.

Phase 3: Composite scoring and report assembly

The composite Compliance Impact Score applies weighted averaging: Legislative Probability 30% + Enforcement Trend 30% + Domino Effect normalized 25% + Tariff Impact 15%. Grade thresholds are CRITICAL ≥75, HIGH ≥50, MODERATE ≥25, LOW below 25. The recommendation string maps to four predefined advisory sentences keyed by score tier: ≥70, ≥45, ≥20, and below.

The final report then assembles secondary intelligence. Federal Register documents are bucketed into proposed/final/notice/other counts. Lobbying filings are grouped by registrant firm using a Map and sorted by count; the top 15 are returned. FEC contributions are grouped by recipient committee or candidate and sorted. The top 15 bill results are mapped to a normalized shape with title, number, status, and sponsor.

Tips for best results

1. Use specific regulatory keywords, not sector names. "PFAS PFOA drinking water" outperforms "chemicals" because specificity targets Federal Register rule titles, bill texts, and OSHA/EPA enforcement rationale fields more precisely. Vague sector names dilute all 13 sources simultaneously.

2. Schedule weekly runs on the same query to detect score movement. A Compliance Impact Score increasing by 15+ points over four weeks is a leading indicator of an accelerating regulatory environment. Static scores on any single run are less actionable than trends.

3. Interpret the individual model scores before reading the composite. A score of 55 driven by high Legislative Probability (90) and low Enforcement Trend (5) means a law is near certain but not yet being enforced — a different risk posture than 55 driven by high Enforcement Trend (80) and low Legislative Probability (20).

4. Use the lobbying firm list to identify industry coalition partners. If 6 firms filed lobbying disclosures on your regulatory topic and you recognize several as competitors, your industry is actively engaging regulators. Join or monitor their coalition before comment periods close.

5. Watch the active agencies list for cascade signals. When the Domino Effect model lists 3+ agencies, standard monitoring is insufficient. Agencies that coordinate enforcement in one sector (e.g., EPA + DOL in chemical manufacturing) tend to align on follow-on actions.

6. Pair with scheduled cost controls. Set a maximum run cost in the Apify scheduler when monitoring 20+ topics. A $2 per-run cap prevents unexpected charges if a single query triggers unusually large data volumes.

7. Export to Google Sheets for time-series tracking. Each weekly run produces one dataset record. The native Apify + Google Sheets integration appends the complianceImpactScore and date to build a regulatory pressure trend chart with no additional tooling.

8. Route CRITICAL COMPLIANCE RISK results through webhooks. Configure a post-run webhook to send a Slack message or create a Jira ticket when a run's score exceeds a threshold. High-risk findings reach the right team without manual review of every run.

Combine with other Apify actors

Limitations

• Results depend on sub-actor data freshness. The Federal Register API is typically current to the prior business day. Congressional bill data may lag 24-72 hours after a floor vote. OSHA and EPA enforcement records can lag several weeks from the actual inspection date.
• Jurisdiction filtering is additive, not exclusive. Appending "California" to the search query increases the probability of state-specific results but does not guarantee all federal results are filtered out. Many agency enforcement databases do not expose geographic filters.
• Scores reflect data volume, not regulatory severity. A high Enforcement Trend score means many enforcement records were returned for the query — not that penalties are large. Two inspections with $1M fines score the same as two inspections with $500 fines.
• The actor does not parse full regulatory text. Federal Register entries are returned with titles, types, agencies, and publish dates — not the full rule text. For full text, run Federal Register Search directly.
• International regulatory sources are not included. All 13 sources are US federal agencies. EU, UK, and other international regulatory databases are out of scope. The jurisdiction field influences which US federal records are returned but does not add international sources.
• Lobbying and FEC data reflect historical filings, not real-time activity. Senate lobbying disclosures are filed quarterly; FEC reports are filed semi-annually or quarterly. Political activity from the past 60-90 days may not yet appear.
• Sub-actor failures produce partial reports. If one or more of the 13 sub-actors fails, the model scores are computed on the remaining data. Scores may be understated. Check dataSources count fields to verify data completeness.
• The actor does not provide legal advice. The Compliance Impact Score and recommendation are quantitative signals derived from public data, not legal opinions. Compliance decisions should be reviewed by qualified legal counsel.

Integrations

• Zapier — trigger a Zapier workflow when a run completes and route CRITICAL COMPLIANCE RISK scores into automated email or task creation workflows
• Make — build multi-step automations that fetch output, compare scores week-over-week, and update a compliance tracking spreadsheet
• Google Sheets — append each weekly run's score and grade to build a time-series regulatory pressure chart
• Apify API — trigger runs from your GRC platform or compliance dashboard and retrieve structured JSON for real-time score display
• Webhooks — configure post-run webhooks to send Slack notifications or create Jira issues when the Compliance Impact Score exceeds a defined threshold
• LangChain / LlamaIndex — feed the structured regulatory output into an LLM pipeline to generate natural language compliance briefings or executive summaries from scored findings

Troubleshooting

• Compliance Impact Score is lower than expected for an active regulatory topic. Check the dataSources field in the output. If sub-actor counts are zero for sources you expected to have data — for example, enforcement.epaEcho: 0 for an environmental topic — the query string may not match agency database terminology. Try synonyms or official regulatory citation formats ("PFOA" instead of "PFAS" for EPA-specific records).

• Run completes but most data source counts are zero. Some regulatory topics — particularly emerging ones — have limited federal database coverage. Niche technical terms may not appear in enforcement databases even when active rulemaking is underway. Broaden your query to a parent category (e.g., "forever chemicals" instead of a specific compound CAS number) to validate that data sources are returning results at all.

• Run is taking longer than 4 minutes. All 13 sub-actors run in parallel with 120-second timeouts. Unusually high data volumes for popular regulatory topics during active Congressional sessions can push total runtime to 3-5 minutes. If runs consistently exceed 5 minutes, open a support issue — per-source maxResults can be adjusted.

• The Domino Effect score is unexpectedly high for a single-agency topic. The Domino Effect model adds Federal Register volume and Congressional bill count bonuses regardless of agency count. A topic with many Federal Register entries and Congressional bills generates a meaningful Domino score even with only one active enforcement agency. This is expected behavior — legislative volume is itself a cascade indicator.

• Lobbying or FEC data is empty despite known industry activity. Senate LDA lobbying disclosures are filed quarterly. If your regulatory topic became active within the past 90 days, filings may not yet appear. FEC contribution data similarly lags by reporting cycle. Re-run in 4-6 weeks for current-cycle data to populate.

Responsible use

• This actor only accesses publicly available data from US federal agency APIs and databases.
• All 13 data sources are official government databases that provide public API access to regulatory, enforcement, and legislative records.
• Comply with applicable data protection and privacy laws when using extracted data in automated workflows or external reports.
• Do not use regulatory data to misrepresent compliance status, fabricate enforcement records, or create misleading regulatory filings.
• The scores produced by this actor are quantitative research tools, not legal opinions. Consult qualified legal counsel before making compliance decisions based on actor output.
• For guidance on web scraping and data access legality, see Apify's guide.

FAQ

How many federal data sources does the regulatory change tracker query in a single run? The actor queries exactly 13 federal data sources in parallel: Federal Register, Congress bills, CBP customs rulings, USITC tariff schedule, SEC EDGAR, OSHA inspections, EPA ECHO enforcement, DOL Wage and Hour enforcement, FDA food recalls, FDA device recalls, Senate lobbying disclosures, FEC campaign finance, and regulatory website change monitoring. All 13 run simultaneously within a single actor execution.

How does regulatory change tracking with this actor differ from manual Federal Register monitoring? The Federal Register alone publishes 80,000+ documents per year. Manual monitoring of a single regulatory topic requires filtering for relevance, tracking bill progress separately, monitoring six enforcement agencies, and aggregating lobbying data — four distinct research tasks across four separate government systems. This actor does all four simultaneously and synthesizes findings into a single scored, graded output in 2-4 minutes.

How is this different from Compliance.ai or LexisNexis Regulatory Compliance? Commercial regulatory intelligence platforms provide curated, human-reviewed regulatory summaries with editorial classification, historical archives, and analyst alert services — typically at $500-2,000/month per user. This actor provides quantified, machine-generated regulatory signals from live government APIs with no subscription commitment. The appropriate use case is automated, programmatic monitoring integrated into development workflows, GRC systems, or scheduled dashboards rather than analyst workstations requiring narrative summaries.

What does the Regulatory Domino Effect model measure and how accurate is it? The Domino Effect model tracks enforcement activity across six federal agencies — OSHA, EPA, DOL-WHD, FDA, SEC, and CBP. Each agency that exceeds a record count threshold adds 10-15 points. When four or more agencies are simultaneously active, a cascade bonus of 20 additional points is applied. The model captures the empirical pattern that when enforcement aligns across agencies in one sector, the same political conditions that drive enforcement at one agency typically affect others. It is a directional signal, not a prediction model.

How accurate is the Legislative Probability score? The Legislative Probability Engine measures legislative momentum through bill stage progression, not predictive modeling based on historical passage rates. Bills at the enacted stage score higher than bills in committee, which is directionally accurate but does not account for political dynamics, rider provisions, or session timing. Use the score as an intensity signal, not a precise probability estimate.

Can I track regulatory changes for non-US jurisdictions? The 13 data sources are all US federal agencies. The jurisdiction field appends a geographic qualifier to the search query but does not add international regulatory sources. For EU, UK, or international regulatory monitoring, the query may surface some cross-border results through Federal Register trade notices and Congressional trade bills, but comprehensive international coverage requires additional actors.

Is it legal to collect data from US federal regulatory databases? Yes. All 13 data sources are official US government databases providing public API access. Federal Register, Congress.gov, OSHA, EPA ECHO, DOL, FDA, SEC EDGAR, CBP, USITC, and FEC make their data publicly available for research, compliance, and journalistic purposes. For a detailed discussion of data collection legality, see Apify's guide.

Can I schedule the regulatory change tracker to run automatically? Yes. Apify's scheduling feature supports cron-based scheduling with daily, weekly, or custom intervals. Running the same query weekly and comparing complianceImpactScore values over time is the most effective way to detect regulatory acceleration before it becomes an enforcement event.

What happens if one of the 13 sub-actors fails during a run? Each sub-actor call is wrapped in an independent error handler. If one source times out or returns an error, the actor logs the failure and continues processing with the remaining 12 sources. The final report is always generated. Identify which sources had issues by checking dataSources count fields — a count of zero for a source that should have data indicates a sub-actor failure during that run.

How long does a typical regulatory change tracking run take? Most runs complete in 2-4 minutes. All 13 sub-actors execute in parallel with a 120-second timeout each, so total runtime is bounded by the slowest single source. Topics with high data volumes — major regulatory topics like AI, climate, or financial regulation — may take 3-5 minutes. Topics with limited federal coverage complete faster.

Can I use the regulatory change tracker output in a GRC platform or compliance dashboard? Yes. The output is structured JSON with consistent field names across all runs, making it straightforward to ingest into GRC platforms, Excel-based compliance dashboards, or BI tools. Use the API with format=json to retrieve results programmatically. The complianceImpactScore, complianceGrade, and recommendation fields are top-level and suitable for summary display without parsing nested model data.

How does the regulatory change tracker score compare across different industries? Scores are relative to the data volume returned for your query, not absolute industry benchmarks. A score of 60 for "cryptocurrency regulation" and a score of 60 for "PFAS chemicals" both indicate HIGH COMPLIANCE RISK, but the underlying data mix will differ. Compare scores from the same query over time — not scores from different queries — to detect meaningful regulatory shifts.

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 — such as additional data sources, custom scoring weights, or sector-specific compliance templates — reach out through the Apify platform.