# FEC Campaign Finance Cleaned (`wakey7dev/fec-finance-normalizer`) Actor

Normalized FEC campaign finance data with employer name standardization, occupation classification, and donor dedup. 200+ employer mappings.

- **URL**: https://apify.com/wakey7dev/fec-finance-normalizer.md
- **Developed by:** [Chris Wakefield](https://apify.com/wakey7dev) (community)
- **Categories:** Business
- **Stats:** 2 total users, 1 monthly users, 100.0% 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

![Chris The Dev](https://raw.githubusercontent.com/chriswakefield87/appstore-screenshot-translator/main/assets/actor-banner.png)

## 🗳️ FEC Campaign Finance Normalizer — Cleaned Contributions & Donor Data

Search US federal campaign finance data from the official FEC OpenFEC API with **normalized employer names**, **standardized occupation categories**, and **deduplicated donors**. Perfect for political research, investigative journalism, compliance screening, and donor analytics.

### ✨ What Makes This Different

Most FEC actors dump raw API data — messy employer names ("GOOGLE INC", "Google", "Alphabet Inc." → all different), vague occupations ("ATTORNEY" vs "Lawyer" vs "ATTY"), and duplicate records. This actor **cleans, normalizes, and enriches** everything:

- 🏢 **200+ employer name normalizations** — Google, Goldman Sachs, Boeing, Pfizer, and more are mapped to canonical names
- 💼 **12 standard occupation categories** — Executive, Finance, Legal, Healthcare, Tech, Real Estate, Education, Government, etc.
- 🧹 **Donor deduplication** — removes duplicate filings for the same contribution
- 📊 **Aggregated summaries** — employer totals and occupation breakdowns included in every run

### 📥 Input Parameters

| Parameter | Type | Default | Description |
|---|---|---|---|
| `searchMode` | select | `donor` | What to search: `donor` (name), `employer` (company), `candidate`, or `committee` |
| `searchQuery` | text | `Google` | The name to search for. For employer mode, try company names like "Goldman Sachs", "Boeing", "Pfizer" |
| `contributorState` | select | All | Filter contributors by US state (e.g. `CA`, `NY`, `TX`) |
| `minAmount` | number | `200` | Minimum contribution amount (FEC itemized threshold is $200) |
| `maxResults` | number | `50` | Maximum contributions to return (1-200) |
| `twoYearCycle` | select | All | Election cycle filter: 2026, 2024, 2022, 2020, 2018, 2016 |

### 📤 Example Input

```json
{
  "searchMode": "employer",
  "searchQuery": "Goldman Sachs",
  "contributorState": "",
  "minAmount": 500,
  "maxResults": 50,
  "twoYearCycle": "2024"
}
```

### 📊 Example Output

```
================================================================================
  FEC CAMPAIGN FINANCE — NORMALIZED RESULTS
================================================================================
  Search Mode:    Employer
  Search Query:   Goldman Sachs
  Retrieved:      50 contributions
  Total Amount:   $287,450.00
  Unique Donors:  42
  Unique Employers: 1
  Unique Committees: 18
  ────────────────────────────────────────────────────────────────────────────
  Employer Total                                    Amount      #
  ────────────────────────────────────────────────────────────────────────────
  Goldman Sachs                                   $287,450.00     50
  ────────────────────────────────────────────────────────────────────────────
  Occupation Category                               Amount      #
  ────────────────────────────────────────────────────────────────────────────
  Executive / C-Suite                             $125,000.00     18
  Finance / Investment                            $98,450.00      22
  Technology / Engineering                         $35,000.00      5
  Legal                                            $29,000.00      5
  ────────────────────────────────────────────────────────────────────────────
  Contributions                                     Amount         Date
  ────────────────────────────────────────────────────────────────────────────
   1. Smith, John A                | Goldman Sachs              $5,800.00   2024-03-15
   2. Doe, Jane M                  | Goldman Sachs              $5,600.00   2024-06-22
  ...
================================================================================
```

### 🎯 Use Cases

- **Political journalists** — trace industry money in politics with clean, comparable data
- **Compliance teams** — screen donors and employers against watchlists
- **Campaign strategists** — analyze competitor fundraising by industry
- **Academic researchers** — study political donation patterns with standardized categories
- **Non-profits / watchdogs** — track corporate political influence

### 🔌 Data Source

All data comes from the official **FEC OpenFEC API** (`api.open.fec.gov`) — free, public, no API key required. This actor uses the Schedule A (individual contributions) endpoint. Data covers all itemized federal contributions ($200+) from 1979 to present.

### 💰 Pricing

Pay-per-event: **$2.00 per 1,000 results** — reflecting the value-add normalization, classification, and deduplication beyond raw API passthrough.

***

*Built by Chris The Dev · [More Actors on Apify Store](https://apify.com/wakey7dev)*

# Actor input Schema

## `searchMode` (type: `string`):

What to search by: donor name, employer/company, candidate name, or committee name.

## `searchQuery` (type: `string`):

Name to search for. For employer mode, try company names like 'Google', 'Goldman Sachs', 'Boeing'. For donor mode, last name or full name.

## `contributorState` (type: `string`):

Filter contributors by US state. Leave blank for all states.

## `minAmount` (type: `integer`):

Only show contributions at or above this amount. Default: 200 (FEC itemized threshold).

## `maxResults` (type: `integer`):

Maximum number of contributions to return (1-200). Default: 50.

## `twoYearCycle` (type: `string`):

Filter to a specific two-year election cycle. Leave blank for all cycles.

## Actor input object example

```json
{
  "searchMode": "donor",
  "searchQuery": "Google",
  "contributorState": "",
  "minAmount": 200,
  "maxResults": 50
}
```

# Actor output Schema

## `results` (type: `string`):

All normalized contribution records as JSON array in the default dataset.

## `summary` (type: `string`):

Human-readable formatted summary table with key statistics.

## `stats` (type: `string`):

Machine-readable statistics 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "searchQuery": "Google"
};

// Run the Actor and wait for it to finish
const run = await client.actor("wakey7dev/fec-finance-normalizer").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 = { "searchQuery": "Google" }

# Run the Actor and wait for it to finish
run = client.actor("wakey7dev/fec-finance-normalizer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "searchQuery": "Google"
}' |
apify call wakey7dev/fec-finance-normalizer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,wakey7dev/fec-finance-normalizer"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/M4Kg4Qwz7dofnIMKB/builds/Y1i8fEg3sLbczJV7j/openapi.json