# Regulatory Arbitrage Detection MCP (`ryanclinton/regulatory-arbitrage-detection-mcp`) Actor Cross-jurisdictional regulatory arbitrage detection and compliance optimization using optimal transport, game theory, and econometric methods. - **URL**: https://apify.com/ryanclinton/regulatory-arbitrage-detection-mcp.md - **Developed by:** [ryan clinton](https://apify.com/ryanclinton) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Regulatory Arbitrage Detection MCP Server **Cross-jurisdictional regulatory arbitrage detection and compliance optimization using optimal transport, game theory, and econometric methods.** This MCP server orchestrates **16 data sources** across federal registers, corporate registries in 6+ jurisdictions, financial regulators, and international databases to power **8 mathematically rigorous tools** for measuring regulatory distance, detecting regulatory capture, estimating lobbying impact, and forecasting regulatory changes. Implements Sinkhorn-Knopp optimal transport, MILP compliance routing, supermodular game theory, Hawkes processes, and Bayesian structural time series. ### What data can you access? | Data Point | Source | |---|---| | Federal regulatory entries and proposed rules | Federal Register | | Congressional bills and legislative activity | Congress Bills | | Consumer financial complaints | CFPB | | UK corporate registrations and filings | UK Companies House | | Global corporate registry data (140+ jurisdictions) | OpenCorporates | | Legal Entity Identifiers and ownership chains | GLEIF LEI | | Canadian corporate registry | Canada Corporations | | New Zealand corporate registry | NZ Companies | | Australian business registry | Australia ABN | | SEC regulatory filings | SEC EDGAR | | US bank financial data | FDIC | | OFAC sanctions lists | OFAC | | Consolidated global sanctions | OpenSanctions | | EU VAT registration verification | EU VAT | | EU trademark registrations | EUIPO | | Federal spending data | USAspending | ### MCP Tools | Tool | Price | Description | |------|-------|-------------| | `measure_regulatory_distance` | $0.04 | Measure pairwise regulatory distance via Sinkhorn-regularized Wasserstein optimal transport | | `optimize_compliance_routing` | $0.04 | Find cost-minimizing cross-jurisdictional compliance routing via MILP branch-and-bound | | `detect_regulatory_capture` | $0.04 | Detect regulatory capture via bipartite C4 clustering coefficient on regulator-industry graph | | `estimate_lobbying_impact` | $0.04 | Estimate causal lobbying impact via Difference-in-Differences with parallel trends test | | `quantify_regulatory_complexity` | $0.04 | Quantify regulatory complexity via Lempel-Ziv compression as Kolmogorov complexity estimator | | `model_regulatory_competition` | $0.04 | Model jurisdictional regulatory competition as supermodular game with Nash equilibrium | | `predict_regulatory_changes` | $0.04 | Predict regulatory changes via Hawkes self-exciting point process with Ogata thinning | | `estimate_causal_regulation_impact` | $0.04 | Estimate causal regulation impact via Bayesian Structural Time Series with Kalman filter | ### Data Sources - **Federal Register** -- US proposed rules, final rules, notices, and executive orders - **Congress Bills** -- Legislative activity, bill status, and committee assignments - **CFPB** -- Consumer Financial Protection Bureau complaint and enforcement data - **UK Companies House** -- UK corporate registrations, officers, and filings - **OpenCorporates** -- Global corporate registry spanning 140+ jurisdictions - **GLEIF LEI** -- Legal Entity Identifier ownership chains and parent-child relationships - **Canada Corporations** -- Canadian federal corporate registry - **NZ Companies** -- New Zealand corporate registry - **Australia ABN** -- Australian Business Number registry - **SEC EDGAR** -- Securities and Exchange Commission filings and analysis - **FDIC Bank** -- Federal Deposit Insurance Corporation bank financial data - **OFAC** -- Office of Foreign Assets Control sanctions lists - **OpenSanctions** -- Consolidated global sanctions and PEP database - **EU VAT** -- European Union VAT registration verification - **EUIPO Trademark** -- EU Intellectual Property Office trademark registrations - **USAspending** -- Federal government spending and contract data ### How the scoring works Each tool implements a distinct mathematical framework: **Sinkhorn-Knopp Optimal Transport** (Tool 1) builds regulation probability distributions per jurisdiction and computes pairwise Wasserstein distances with entropic regularization. The Gibbs kernel K_ij = exp(-C_ij/epsilon) is computed with alternating u/v scaling until convergence. Larger distances indicate greater regulatory divergence and arbitrage potential. **MILP Compliance Routing** (Tool 2) formulates cross-jurisdictional compliance as a Mixed-Integer Linear Program: minimize total compliance cost subject to binary jurisdiction selection variables. Branch-and-bound enumeration finds the globally optimal route. **Bipartite C4 Capture Detection** (Tool 3) builds a regulator-industry bipartite graph and counts 4-cycles through each regulator node. Results are compared to a null model (100 Monte Carlo iterations of random bipartite graphs with preserved degree sequences). Z-scores significantly above the null indicate capture risk. **Difference-in-Differences** (Tool 4) partitions jurisdictions into treatment (with lobbying activity) and control groups. The treatment effect tau is computed with standard errors, t-statistics, and p-values. A parallel trends test validates the identification assumption. **Lempel-Ziv Complexity** (Tool 5) estimates Kolmogorov complexity per jurisdiction by counting distinct substrings in sequential parsing of regulatory text representations. Compression ratios and Gini coefficients quantify complexity inequality across jurisdictions. **Supermodular Game** (Tool 6) models N jurisdictions as players choosing regulatory levels with positive cross-derivatives (strategic complements). Best-response iteration converges to Nash equilibrium by Topkis' theorem. The race-to-bottom index measures how far the equilibrium falls below the Pareto optimum. **Hawkes Process** (Tool 7) models regulatory event intensity as self-exciting: past events increase future event probability. The branching ratio (spectral radius of alpha/beta) approaching 1 signals critical instability. Ogata thinning generates forward simulations. **Bayesian Structural Time Series** (Tool 8) decomposes regulatory impact into level, trend, and seasonal components via Kalman filtering. Counterfactual extrapolation estimates treatment effects with Bayesian credible intervals. ### How to connect this MCP server #### Claude Desktop Add to your `claude_desktop_config.json`: ```json { "mcpServers": { "regulatory-arbitrage-detection": { "url": "https://regulatory-arbitrage-detection-mcp.apify.actor/mcp" } } } ```` #### Programmatic (HTTP) ```bash curl -X POST https://regulatory-arbitrage-detection-mcp.apify.actor/mcp \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_APIFY_TOKEN" \ -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"measure_regulatory_distance","arguments":{"query":"banking regulation EU US UK"}},"id":1}' ``` This MCP also works with **Cursor**, **Windsurf**, **Cline**, and any other MCP-compatible client. ### Use cases for regulatory arbitrage intelligence #### Cross-Border Compliance Strategy Measure regulatory distance between jurisdictions to identify where compliance programs can be harmonized and where jurisdiction-specific requirements demand separate approaches. #### Regulatory Capture Risk Assessment Screen regulators for capture indicators using network analysis. Identify revolving door patterns, rule weakening trends, and regulator-industry clustering that signal compromised oversight. #### Lobbying ROI Analysis Estimate the causal impact of lobbying expenditure on regulatory outcomes using econometric methods. Determine whether lobbying investments are producing measurable regulatory changes. #### Regulatory Complexity Benchmarking Compare regulatory burden across jurisdictions using information-theoretic complexity measures. Identify over-complex regulatory regimes that may benefit from simplification. #### Race-to-Bottom Early Warning Model competitive dynamics between jurisdictions to detect whether regulatory competition is producing a race to the bottom. Quantify the welfare gap between Nash equilibrium and Pareto optimum. #### Regulatory Change Forecasting Predict when and where regulatory changes will occur using self-exciting point process models. Identify sectors where regulatory event clustering signals imminent new rules. ### How much does it cost? This MCP uses **pay-per-event** pricing. Each tool call costs $0.04. The **Apify Free plan** includes $5 of monthly platform credits, which covers 125 tool calls. | Example Use | Approximate Cost | |---|---| | Measure regulatory distance between two jurisdictions | $0.04 | | Detect regulatory capture in a sector | $0.04 | | Full suite of 8 analyses for one topic | $0.32 | ### How it works 1. **You call a tool** with a regulatory query and optional data source category filters (regulatory, corporate, financial, international, spending) 2. **Up to 16 Apify actors run in parallel** fetching data from federal registers, corporate registries, financial regulators, and international databases 3. **A regulatory network is constructed** with entity resolution linking records across jurisdictions 4. **The mathematical model runs** on the network -- optimal transport, MILP, game theory, or econometric analysis 5. **Structured results are returned** with model outputs, statistical diagnostics, and supporting data ### FAQ **Q: What jurisdictions are covered?** A: The system queries corporate registries in the US, UK, Canada, New Zealand, Australia, and the EU, plus global corporate data via OpenCorporates (140+ jurisdictions). Federal regulatory data is US-focused. **Q: How reliable are the causal estimates?** A: The Difference-in-Differences and BSTS tools implement standard econometric identification strategies with diagnostic tests (parallel trends, Bayesian credible intervals). Results should be interpreted as estimates subject to the usual causal inference assumptions. **Q: Can this detect illegal regulatory arbitrage?** A: The system identifies regulatory distance and arbitrage opportunities. It does not make legal determinations about whether specific arbitrage strategies are lawful. **Q: Is it legal to use this data?** A: All data sources are publicly available government registries and open databases. See [Apify's guide on web scraping legality](https://blog.apify.com/is-web-scraping-legal/). **Q: What is the Hawkes process branching ratio?** A: The branching ratio measures self-excitation intensity. Values near 0 indicate independent events; values approaching 1 indicate critical instability where each event triggers more events. Values above 0.8 are concerning. **Q: Can I combine this with other MCPs?** A: Yes. Use alongside the Regulatory Change Intelligence MCP for US federal regulatory monitoring or the UK Regulatory Ecosystem MCP for UK-specific compliance analysis. ### Related MCP servers | MCP Server | Description | |---|---| | [ryanclinton/regulatory-change-intelligence-mcp](https://apify.com/ryanclinton/regulatory-change-intelligence-mcp) | US federal regulatory pipeline monitoring | | [ryanclinton/uk-regulatory-ecosystem-mcp](https://apify.com/ryanclinton/uk-regulatory-ecosystem-mcp) | UK multi-agency regulatory intelligence | | [ryanclinton/sanctions-evasion-network-mcp](https://apify.com/ryanclinton/sanctions-evasion-network-mcp) | Structural sanctions evasion detection | ### Integrations This MCP server is built on the **Apify platform** and supports: - **Apify API** for programmatic access and regulatory monitoring automation - **Scheduled runs** via Apify Scheduler for recurring regulatory analysis - **Webhooks** for triggering alerts when regulatory distances or capture scores change - **Integration with 200+ Apify actors** for extending jurisdictional coverage # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ryanclinton/regulatory-arbitrage-detection-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("ryanclinton/regulatory-arbitrage-detection-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call ryanclinton/regulatory-arbitrage-detection-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ryanclinton/regulatory-arbitrage-detection-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Regulatory Arbitrage Detection MCP", "description": "Cross-jurisdictional regulatory arbitrage detection and compliance optimization using optimal transport, game theory, and econometric methods.", "version": "1.0", "x-build-id": "JVtDNZlUlpvfLDO3d" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ryanclinton~regulatory-arbitrage-detection-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ryanclinton-regulatory-arbitrage-detection-mcp", 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"runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, 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