# Fake Candidate Checker – LinkedIn Fraud Risk Analyzer (`established_zabra/my-actor`) Actor

Analyze LinkedIn profiles for potential hiring fraud risk using metadata-based heuristics and explainable scoring. Works even when LinkedIn access is restricted by falling back to non-invasive analysis. Provides risk indicators to help recruiters decide when enhanced screening is needed.

- **URL**: https://apify.com/established\_zabra/my-actor.md
- **Developed by:** [Yagnesh Patel](https://apify.com/established_zabra) (community)
- **Categories:** Jobs, AI, Other
- **Stats:** 3 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Fake Candidate Checker – LinkedIn Fraud Risk Analyzer

### Overview

The **Fake Candidate Checker** is an Apify actor designed to analyze LinkedIn profiles for **potential hiring fraud risk indicators** using **metadata-based heuristics**, explainable scoring, and optional limited public access checks.

The actor is built to remain reliable even when LinkedIn restricts automated access by falling back to **non-invasive metadata-only analysis** instead of failing or attempting aggressive scraping.

This makes it suitable for **HR teams, recruiters, staffing agencies, and hiring managers** who want early signals about profiles that may require additional verification.

***

### What Problem Does This Solve?

Modern hiring processes increasingly face challenges such as:

- Fake or misrepresented candidates
- Auto-generated or incomplete LinkedIn profiles
- Inconsistent public profile signals
- Increased risk in remote hiring workflows
- Wasted interview and onboarding effort

This actor helps teams **identify profiles that may require enhanced screening** before progressing further in the hiring pipeline.

***

### Key Features

- ✅ Metadata-only analysis (no LinkedIn login required)
- ✅ Graceful handling of LinkedIn access restrictions
- ✅ Explainable risk scores with clear flags
- ✅ Store-safe, non-invasive design
- ✅ Works reliably even when LinkedIn blocks access
- ✅ Suitable for compliance-sensitive environments

***

### How It Works

1. Accepts one or more LinkedIn profile URLs
2. Optionally attempts limited public data access
3. If LinkedIn restricts access, switches to metadata-only heuristics
4. Assigns a probabilistic **risk score**
5. Outputs detected risk indicators and recommended next steps

***

### Input

#### Example Input

````json
{
  "linkedinUrls": [
    "https://www.linkedin.com/in/example-profile/"
  ],
  "metadataOnly": true
}

#### Example Outputs
{
  "linkedinUrl": "https://www.linkedin.com/in/example-profile/",
  "mode": "metadata-only",
  "riskScore": 45,
  "flags": [
    "Limited public LinkedIn visibility",
    "Suspicious numeric-heavy profile handle"
  ],
  "recommendation": "Manual review recommended",
  "disclaimer": "This tool provides probabilistic risk indicators only and does not confirm fraud."
}

# Actor input Schema

## `linkedinUrls` (type: `array`):

List of public LinkedIn profile URLs to analyze
## `metadataOnly` (type: `boolean`):

If enabled, the actor will not visit LinkedIn pages and will analyze URLs using metadata and fallback heuristics only.

## Actor input object example

```json
{
  "metadataOnly": false
}
````

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("established_zabra/my-actor").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("established_zabra/my-actor").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 established_zabra/my-actor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=established_zabra/my-actor",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/owErZg6KCffzbazBP/builds/o6JuWW0j4ijGrx4xf/openapi.json