# Wanted List Screening: FBI, Interpol & Europol (`jungle_synthesizer/wanted-list-scraper`) Actor Criminal wanted-list screening: FBI (all categories), Interpol Red Notices, Europol. Bulk ingest, fuzzy name-match with score, or new-listings diff. Active/captured status, reward amounts, charges, physical descriptions, photo URLs. For KYC, investigations, OSINT, journalism, bounty hunting. - **URL**: https://apify.com/jungle\_synthesizer/wanted-list-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Business, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Wanted List Screening: FBI, Interpol & Europol Screen individuals against public criminal wanted lists from three major law-enforcement agencies — FBI, Interpol, and Europol — in a single run. Designed for KYC workflows, compliance screening, OSINT investigations, and journalism. ### What it does The actor pulls public wanted-notice data from: - **Interpol** — Red Notices via the public REST API (`ws-public.interpol.int`) - **Europol** — EU Most Wanted listings via `eumostwanted.eu` - **FBI** — Most Wanted, Cyber, Counterterrorism, and other categories via `fbi.gov/wanted/api/` It operates in three modes: | Mode | Description | |------|-------------| | `ingest_lists` | Bulk-load all active notices from the selected agencies into the dataset | | `screen_queries` | Fuzzy name-match a list of names against all notices; returns hits with match scores | | `new_listings_diff` | Compare the current notice set against the previous run's snapshot; output only newly published entries | ### Output fields Each record includes: | Field | Description | |-------|-------------| | `notice_id` | Canonical cross-agency ID (`agency:source_id`) | | `source_agency` | `fbi`, `interpol`, or `europol` | | `source_notice_id` | Agency-native notice ID | | `source_url` | Direct URL to the notice on the source site | | `notice_type` | Notice type: `most_wanted`, `red`, `cyber`, `counterterrorism`, etc. | | `primary_name` | Full name of the wanted individual | | `alias_names` | Known aliases (pipe-separated) | | `date_of_birth` | Date of birth (YYYY-MM-DD where available) | | `nationalities` | Nationalities (pipe-separated ISO codes or country names) | | `photographs` | Photo URLs (pipe-separated) | | `charges` | Criminal charges (pipe-separated) | | `case_summary` | Brief description of the case | | `reward_amount_usd` | Reward offered in USD (numeric) | | `last_known_location` | Last known location or jurisdiction | | `status` | `active`, `captured`, `deceased`, `located`, or `removed` | | `publication_date` | Date the notice was first published | | `query_term` | Query used (screen_queries mode only) | | `match_score` | Fuzzy match score 0-100 (screen_queries mode only) | | `match_reason` | Human-readable match explanation (screen_queries mode only) | See the full dataset schema for all 35 fields. ### Input ```json { "mode": "ingest_lists", "agencies": ["interpol", "europol", "fbi"], "noticeTypes": ["red", "most_wanted"], "maxItems": 100, "includeRemoved": false, "sp_intended_usage": "KYC compliance screening", "sp_improvement_suggestions": "none" } ```` #### Key parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `mode` | string | `ingest_lists` | Operating mode (see table above) | | `agencies` | array | all | Agencies to include: `fbi`, `interpol`, `europol` | | `noticeTypes` | array | all | Notice types to filter (e.g. `red`, `most_wanted`, `cyber`) | | `maxItems` | integer | 10 | Maximum records to return. `0` = no limit | | `includeRemoved` | boolean | false | Include captured or removed entries | | `minRewardUsd` | integer | 0 | Filter to listings with reward >= this amount | | `queries` | array | — | Names to fuzzy-match (screen\_queries mode only) | | `minMatchScore` | integer | 75 | Minimum fuzzy match score 0-100 (screen\_queries mode only) | ### Use cases - **KYC / AML screening** — check customer names against international wanted lists before onboarding - **Compliance monitoring** — run `new_listings_diff` on a schedule to detect newly posted notices - **OSINT investigations** — bulk-load public wanted data for research, linking, and analysis - **Journalism** — pull structured data for story research without manual browsing - **Bounty tracking** — filter by `minRewardUsd` to surface high-value notices ### Notes - **FBI data** is fetched via Playwright with residential proxy to bypass Cloudflare protection on `fbi.gov`. - **Interpol** uses the public REST API with browser-like headers; residential proxy is used in cloud runs. - **Europol** scrapes structured HTML from the EU Most Wanted site. - All three sources serve publicly available, government-published data. No authentication or API key is required. - Array values (aliases, nationalities, photographs, charges) are stored as pipe-separated strings. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `mode` (type: `string`): No description ## `agencies` (type: `array`): Subset of: fbi, interpol, europol. Empty = all. ## `noticeTypes` (type: `array`): Subset of: most\_wanted, red, counterterrorism, cyber, crimes\_against\_children, fugitive. Empty = all. ## `includeRemoved` (type: `boolean`): No description ## `minRewardUsd` (type: `integer`): Filter to listings with reward >= this amount. 0 = no filter. ## `queries` (type: `array`): Names to fuzzy-match against wanted lists. ## `minMatchScore` (type: `integer`): No description ## `maxItems` (type: `integer`): Maximum records to return. 0 = no limit. ## `proxyConfiguration` (type: `object`): No description ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "ingest_lists", "agencies": [ "fbi", "interpol", "europol" ], "noticeTypes": [ "most_wanted", "red" ], "queries": [ "test_query_placeholder" ], "minMatchScore": 75, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "ingest_lists", "agencies": [ "fbi", "interpol", "europol" ], "noticeTypes": [ "most_wanted", "red" ], "includeRemoved": false, "minRewardUsd": 0, "queries": [ "test_query_placeholder" ], "minMatchScore": 75, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/wanted-list-scraper").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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "ingest_lists", "agencies": [ "fbi", "interpol", "europol", ], "noticeTypes": [ "most_wanted", "red", ], "includeRemoved": False, "minRewardUsd": 0, "queries": ["test_query_placeholder"], "minMatchScore": 75, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/wanted-list-scraper").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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "ingest_lists", "agencies": [ "fbi", "interpol", "europol" ], "noticeTypes": [ "most_wanted", "red" ], "includeRemoved": false, "minRewardUsd": 0, "queries": [ "test_query_placeholder" ], "minMatchScore": 75, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call jungle_synthesizer/wanted-list-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/wanted-list-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wanted List Screening: FBI, Interpol & Europol", "description": "Criminal wanted-list screening: FBI (all categories), Interpol Red Notices, Europol. Bulk ingest, fuzzy name-match with score, or new-listings diff. Active/captured status, reward amounts, charges, physical descriptions, photo URLs. For KYC, investigations, OSINT, journalism, bounty hunting.", "version": "0.1", "x-build-id": "haKfWyXzGzL1heiNS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~wanted-list-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-wanted-list-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~wanted-list-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-wanted-list-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~wanted-list-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-wanted-list-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "sp_intended_usage", "sp_improvement_suggestions" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "mode": { "title": "Mode", "enum": [ "ingest_lists", "screen_queries", "new_listings_diff" ], "type": "string", "description": "" }, "agencies": { "title": "Agencies to Include", "type": "array", "description": "Subset of: fbi, interpol, europol. Empty = all.", "items": { "type": "string" } }, "noticeTypes": { "title": "Notice Types", "type": "array", "description": "Subset of: most_wanted, red, counterterrorism, cyber, crimes_against_children, fugitive. Empty = all.", "items": { "type": "string" } }, "includeRemoved": { "title": "Include Captured/Removed Entries", "type": "boolean", "description": "" }, "minRewardUsd": { "title": "Minimum Reward (USD)", "type": "integer", "description": "Filter to listings with reward >= this amount. 0 = no filter." }, "queries": { "title": "Names to Screen (screen_queries mode)", "type": "array", "description": "Names to fuzzy-match against wanted lists.", "items": { "type": "string" } }, "minMatchScore": { "title": "Min Fuzzy Match Score", "type": "integer", "description": "" }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum records to return. 0 = no limit.", "default": 10 }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "" } } }, "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" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```