Financial Crime Screening MCP Server

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Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "financial-crime-screening-mcp": {
      "url": "https://ryanclinton--financial-crime-screening-mcp.apify.actor/mcp"
    }
  }
}

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Financial crime screening MCP server that connects your AI agent or compliance workflow to 13 live data sources for AML/CFT due diligence. Give it an entity name and it returns a structured AML Risk Tier — LOW, MEDIUM, HIGH, or PROHIBITED — with dimensional scores, SAR filing recommendations, and supporting evidence. Purpose-built for banks, crypto exchanges, money service businesses, and compliance teams that need audit-ready risk classifications on demand.

The server runs on Apify's Standby infrastructure and exposes 8 MCP tools via a persistent /mcp endpoint. Each tool dispatches parallel requests to the underlying actor fleet — sanctions databases, criminal watchlists, foreign agent registries, corporate registries, and financial regulators — then applies a five-dimensional AML scoring engine to produce quantified, explainable risk output. No subscriptions. No monthly minimums. You pay per tool call.

What data can you access?

| Data Point | Source | Example |
|---|---|---|
| 📋 US Treasury SDN and blocked persons | OFAC Sanctions | "Meridian Trade LLC" — exact match, score 1.00 |
| 🌐 Global sanctions, PEPs, and watchlists | OpenSanctions (100+ lists) | EU consolidated list, UN Security Council |
| 🚨 International wanted persons | Interpol Red Notices | Charges: wire fraud, money laundering |
| 🔴 US federal wanted persons | FBI Most Wanted | Fugitive status, last known location |
| 🏛️ Foreign agent registrations | FARA DOJ Registry | Foreign principal, country of origin |
| 🗳️ Political contribution records | FEC Campaign Finance | $485,000 in contributions — PEP indicator |
| 🏢 Corporate registry (140+ jurisdictions) | OpenCorporates (200M+ records) | Jurisdiction: VG, status: dissolved |
| 🔑 Legal entity identification | GLEIF LEI Database | LEI: 549300ABCD1234567890 |
| 🤝 Nonprofit 990 financial data | ProPublica Nonprofit Explorer | IRS revocation status |
| ⚠️ Consumer financial complaints | CFPB Complaint Database | 73 complaints — pattern of harm |
| 🏦 US bank institution verification | FDIC Bank Data | Active/inactive insurance status |
| 📑 SEC regulatory filings | SEC EDGAR | 8-K, SC 13D, enforcement filings |
| 📈 Insider transaction disclosures | SEC Form 4 Insider Trading | Sale/buy ratio — 87% sales |

Why use Financial Crime Screening MCP Server?

Manual AML screening for a single entity requires opening six or more government portals, running name searches with spelling variants, recording results in a spreadsheet, and repeating the process periodically. A trained compliance analyst spends 45-90 minutes per entity on initial onboarding. With high-volume customer pipelines or transaction monitoring queues, that throughput does not scale.

Vendors like Dow Jones Risk and Refinitiv World-Check charge $15,000-60,000 per year for subscription access to similar data. This MCP gives you programmatic access to the same primary sources — OFAC, Interpol, FBI, FARA, OpenSanctions, SEC, FDIC, CFPB — for a few dollars per screen, with no minimum commitment.

- Scheduling — run periodic re-screening on watchlist changes or on a compliance calendar via Apify's built-in scheduler
- API access — trigger screenings from Python, JavaScript, Claude Desktop, Cursor, or any MCP-compatible client
- Parallel execution — all actor calls within each tool run in parallel, returning results in seconds rather than minutes
- Monitoring — get Slack or email alerts when screening runs complete or return unexpected results
- Integrations — connect results to case management systems, Google Sheets, HubSpot, or webhooks

Features

- Eight focused MCP tools covering the full AML screening workflow: entity screen, sanctions deep check, criminal watchlist scan, PEP analysis, shell detection, financial institution verification, proximity scoring, and full AML classification
- 13 live data sources queried in real time — results always reflect the current published state of each database, not a cached copy
- Five-dimensional AML scoring engine allocating points across Sanctions Exposure (0-35), Corporate Transparency (0-25), Political Exposure (0-20), Financial Regulatory Standing (0-10), and Proximity to Crime (0-10) for a total score of 0-100
- Automatic PROHIBITED escalation — any direct sanctions match with confidence >= 0.95 triggers PROHIBITED tier regardless of the composite score, with a SAR filing mandate
- Fuzzy name matching — OFAC and OpenSanctions hits are separated into exact matches (score >= 0.95) and fuzzy matches (0.50-0.95) for prioritized review queues
- Shell company detection using seven textual indicators (registered agent, nominee, bearer shares, trust company, corporate services, shelf company, formation agent) across OpenCorporates records
- Shell haven jurisdiction check against 12 known offshore jurisdictions: Panama (PA), British Virgin Islands (VG), Cayman Islands (KY), Belize (BZ), Seychelles (SC), Samoa (WS), Vanuatu (VU), Marshall Islands (MH), Bermuda (BM), Jersey (JE), Guernsey (GG), Isle of Man (IM)
- Proximity-to-crime convergence scoring — each adverse signal category that activates adds 17 points to a 0-100 convergence score; four or more categories converging triggers CRITICAL classification
- PEP detection via FEC threshold logic — campaign contributions above $10,000 trigger potential PEP flag; above $100,000 trigger confirmed PEP classification
- FARA foreign agent mapping — identifies registered foreign agents with their foreign principals and country of origin
- Nonprofit money laundering flag — IRS-revoked nonprofit status is treated as a distinct AML red flag
- Spending limit enforcement — every tool checks Actor.charge() before execution and returns a structured error if the per-run budget ceiling is reached
- Stateless Streamable HTTP transport — each POST to /mcp instantiates a fresh McpServer with no session state, enabling horizontal scaling

Use cases for financial crime screening

Bank customer onboarding and KYC

Compliance teams at banks and credit unions use comprehensive_entity_screen at account opening to check new applicants against OFAC, OpenSanctions, Interpol, and FBI simultaneously. The structured output feeds directly into the onboarding case file, reducing analyst time from 60 minutes to under 5 minutes per customer while producing an auditable record.

Crypto exchange AML and FinCEN compliance

Cryptocurrency exchanges subject to FinCEN's MSB rules and the EU's MiCA regulation use aml_risk_classification before enabling withdrawals or high-value trading. The PROHIBITED tier output, combined with the sarRequired flag and SAR narrative guidance, satisfies recordkeeping obligations under the Bank Secrecy Act.

Correspondent banking due diligence

Respondent banks seeking to establish correspondent relationships require enhanced due diligence under FATF Recommendation 13. financial_institution_verify cross-checks FDIC insurance status, corporate registration, and CFPB complaint density to surface institutions that may be impersonating regulated banks or have patterns of consumer harm.

Transaction monitoring and SAR investigation

Compliance investigators using proximity_to_crime_score can triage transaction monitoring alerts by convergence level before committing analyst time to full investigation. A CRITICAL convergence score (four or more adverse signal categories) escalates directly to SAR preparation; a NONE score clears the alert without manual review.

PEP screening for high-value accounts

Private banks and wealth managers required to identify politically exposed persons use pep_influence_analysis to check FARA registrations and FEC contribution totals. The tool returns a PEP classification with supporting evidence and a recommendation for enhanced due diligence, source-of-wealth verification, and senior management approval.

Shell company investigation and beneficial ownership

Corporate investigators and FinCEN examiners use corporate_shell_detection to identify potential layering structures before processing wire transfers. The tool produces a shell risk score (0-100) with itemized indicator findings, supporting beneficial ownership documentation requests under the CDD Rule.

How to screen an entity for financial crime

1. Connect your MCP client — Add the server URL https://ryanclinton--financial-crime-screening-mcp.apify.actor/mcp to Claude Desktop, Cursor, Windsurf, or any MCP-compatible AI agent.
2. Choose the right tool — Start with comprehensive_entity_screen for initial onboarding, or aml_risk_classification when you need a full documented risk determination with dimensional scores.
3. Run the screening — Pass the entity name and type (individual/company). The server dispatches up to 13 parallel actor calls and typically returns results within 30-90 seconds.
4. Review the AML Risk Tier — The response includes a tier (LOW/MEDIUM/HIGH/PROHIBITED), a numeric score, dimensional breakdowns, a SAR recommendation, and all supporting evidence for your compliance file.

Input parameters

This is an MCP server — there are no traditional actor input fields. Each tool accepts its own parameters as defined below.

Tool parameters

| Tool | Parameter | Type | Required | Default | Description |
|---|---|---|---|---|---|
| comprehensive_entity_screen | entity_name | string | Yes | — | Name of the person or company to screen |
| comprehensive_entity_screen | entity_type | enum | No | unknown | individual, company, or unknown |
| comprehensive_entity_screen | country | string | No | — | Two-letter country code hint (e.g. US, GB) |
| sanctions_deep_check | entity_name | string | Yes | — | Name to check against OFAC and OpenSanctions |
| sanctions_deep_check | include_aliases | boolean | No | true | Search known aliases and transliterations |
| criminal_watchlist_scan | name | string | Yes | — | Name to search in Interpol and FBI databases |
| criminal_watchlist_scan | nationality | string | No | — | Nationality hint to narrow Interpol search |
| pep_influence_analysis | name | string | Yes | — | Person or organization name |
| pep_influence_analysis | include_campaign_finance | boolean | No | true | Include FEC campaign finance records |
| corporate_shell_detection | company_name | string | Yes | — | Company name to analyze for shell indicators |
| corporate_shell_detection | jurisdiction | string | No | — | Known jurisdiction of the company |
| financial_institution_verify | institution_name | string | Yes | — | Name of the financial institution |
| financial_institution_verify | include_complaints | boolean | No | true | Include CFPB consumer complaint analysis |
| proximity_to_crime_score | entity_name | string | Yes | — | Entity to score for signal convergence |
| proximity_to_crime_score | entity_type | enum | No | unknown | individual, company, or unknown |
| aml_risk_classification | entity_name | string | Yes | — | Entity name to classify |
| aml_risk_classification | entity_type | enum | No | unknown | individual, company, or unknown |
| aml_risk_classification | country | string | No | — | Country code hint |

Connection configuration examples

Claude Desktop (claude_desktop_config.json):

{

  "mcpServers": {

    "financial-crime-screening": {

      "url": "https://ryanclinton--financial-crime-screening-mcp.apify.actor/mcp",

      "headers": {

        "Authorization": "Bearer YOUR_APIFY_TOKEN"

      }

    }

  }

}

Cursor / Windsurf / Cline (.cursor/mcp.json or equivalent):

{

  "mcpServers": {

    "financial-crime-screening": {

      "url": "https://ryanclinton--financial-crime-screening-mcp.apify.actor/mcp",

      "headers": {

        "Authorization": "Bearer YOUR_APIFY_TOKEN"

      }

    }

  }

}

Direct HTTP call for individual tool:

curl -X POST "https://ryanclinton--financial-crime-screening-mcp.apify.actor/mcp" \

  -H "Content-Type: application/json" \

  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \

  -d '{

    "jsonrpc": "2.0",

    "method": "tools/call",

    "params": {

      "name": "aml_risk_classification",

      "arguments": {

        "entity_name": "Meridian Trade & Finance LLC",

        "entity_type": "company",

        "country": "PA"

      }

    },

    "id": 1

  }'

Usage tips

- Match entity type to the search — pass entity_type: "individual" for persons to skip LEI, OpenCorporates, and FDIC lookups, which reduces cost and latency.
- Use comprehensive_entity_screen for initial onboarding and reserve aml_risk_classification for final risk determinations that require a documented dimensional score.
- Country code hints narrow results — passing country: "VG" when you already know an entity is BVI-registered improves match precision across corporate registries.
- Batch via the Apify API — for high-volume screening queues, call the MCP tools programmatically from Python or JavaScript with Promise.all across multiple entities.
- Store the full JSON response — the structured output with dimensional scores is designed to serve as the analytical record in a compliance case file.

Output example

Below is a representative response from aml_risk_classification for a hypothetical high-risk entity.

{
  "entity": "Meridian Trade & Finance LLC",
  "entityType": "company",
  "amlRiskTier": "PROHIBITED",
  "riskScore": 82,
  "directSanctionsMatch": true,
  "sarRequired": true,
  "dimensions": {
    "sanctionsWatchlist": {
      "score": 35,
      "max": 35,
      "findings": [
        "1 EXACT sanctions match — OFAC/OpenSanctions. PROHIBITED.",
        "3 fuzzy sanctions match(es) — manual review required"
      ]
    },
    "corporateTransparency": {
      "score": 20,
      "max": 25,
      "findings": [
        "Shell company indicators detected: registered agent, nominee",
        "Registered in known shell company jurisdiction: VG",
        "Entity incorporated only 2 months ago — high-risk age for AML"
      ]
    },
    "politicalExposure": {
      "score": 12,
      "max": 20,
      "findings": [
        "Registered foreign agent under FARA — principals: Government of Ruritania"
      ]
    },
    "financialRegulatory": {
      "score": 5,
      "max": 10,
      "findings": [
        "FDIC record shows INACTIVE/CLOSED institution — entity may be impersonating a bank"
      ]
    },
    "proximityCrime": {
      "score": 10,
      "max": 10,
      "findings": [
        "CRITICAL CONVERGENCE: 4 adverse signal categories (sanctions, foreign agent, shell indicators, consumer complaints) — strong proximity to financial crime"
      ]
    }
  },
  "recommendation": "TRANSACTION MUST BE BLOCKED. File Suspicious Activity Report (SAR) within 30 days. Do not tip off the subject. Escalate to BSA/AML officer immediately.",
  "actorsUsed": 13
}

Output fields

| Field | Type | Description |
|---|---|---|
| entity | string | Entity name as submitted |
| entityType | string | individual, company, or unknown |
| amlRiskTier | string | LOW, MEDIUM, HIGH, or PROHIBITED |
| riskScore | number | Composite AML score 0-100 |
| directSanctionsMatch | boolean | True if any sanctions hit has confidence >= 0.95 or exact match |
| sarRequired | boolean | True for HIGH and PROHIBITED tiers |
| dimensions.sanctionsWatchlist.score | number | Points scored in sanctions dimension (max 35) |
| dimensions.sanctionsWatchlist.findings | array | Human-readable finding strings for each signal |
| dimensions.corporateTransparency.score | number | Points scored in corporate transparency dimension (max 25) |
| dimensions.corporateTransparency.findings | array | Shell indicators, jurisdiction flags, status issues |
| dimensions.politicalExposure.score | number | Points scored in PEP dimension (max 20) |
| dimensions.politicalExposure.findings | array | FARA registrations, FEC contribution totals |
| dimensions.financialRegulatory.score | number | Points scored in regulatory standing dimension (max 10) |
| dimensions.financialRegulatory.findings | array | FDIC status, CFPB complaint density, insider trading ratio |
| dimensions.proximityCrime.score | number | Signal convergence score (max 10) |
| dimensions.proximityCrime.findings | array | Convergence level and active category list |
| recommendation | string | Tier-specific compliance action (block, file SAR, enhanced DD, standard processing) |
| actorsUsed | number | Number of underlying actors called in this run |

For sanctions_deep_check, additional fields include blocked (boolean), summary (verdict string), exactMatches (array), and fuzzyMatches (array). For proximity_to_crime_score, additional fields include proximityScore (0-100), convergenceLevel (NONE/LOW/MODERATE/HIGH/CRITICAL), activeSignals (count), and breakdown (per-category array).

How much does it cost to screen entities for financial crime?

Financial Crime Screening MCP Server uses pay-per-event pricing — you pay $0.045 per tool call. There is no subscription, no monthly minimum, and no charge for idle standby time.

| Scenario | Tool calls | Cost per call | Total cost |
|---|---|---|---|
| Quick sanctions check | 1 | $0.045 | $0.045 |
| Initial onboarding (screen + shell + PEP) | 3 | $0.045 | $0.135 |
| Full AML classification (13 actors) | 1 | $0.045 | $0.045 |
| Daily re-screening of 100 entities | 100 | $0.045 | $4.50 |
| Monthly batch of 1,000 entities | 1,000 | $0.045 | $45.00 |

You can set a maximum spending limit per run to control costs. The server checks your budget ceiling before each tool execution and returns a structured error if the limit is reached rather than continuing to charge.

Apify's free tier includes $5 of monthly platform credits — enough to run approximately 110 full AML classifications with no payment required.

Compare this to Dow Jones Risk Center or Refinitiv World-Check at $15,000-60,000 per year for subscription access to the same primary data sources. With this MCP, most compliance teams running 500-2,000 screenings per month spend $22-$90/month with no commitment.

Financial crime screening using the API

Python

client = ApifyClient("YOUR_API_TOKEN")