# USAspending Loans Scraper | Federal Loan Programs Data (`parseforge/usaspending-loans-scraper`) Actor
Export federal loan and loan guarantee awards from USAspending.gov: recipient, awarding agency, loan value, subsidy cost, CFDA number, issued date and description. Filter by recipient name, fiscal year and agency. CSV, Excel, JSON or XML for credit research, compliance and policy work.
- **URL**: https://apify.com/parseforge/usaspending-loans-scraper.md
- **Developed by:** [ParseForge](https://apify.com/parseforge) (community)
- **Categories:** Other, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $19.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
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
## ๐ฆ USAspending Loans Scraper
> ๐ **Export federal loan and loan guarantee awards in seconds. Recipients, agencies, loan value, subsidy cost โ straight to CSV, Excel, JSON, or XML.**
> ๐ **Last updated:** 2026-05-26 ยท **๐ 11 fields** per record ยท **Federal scale** ยท **All federal loan programs**
USAspending.gov is the official source for federal financial assistance. This actor focuses on loan programs (award codes 07 and 08), turning USAspending search results into a clean dataset with one row per loan award.
Coverage spans every federal loan program (Small Business Administration, Department of Education direct loans, USDA Rural Development, etc.), every fiscal year from 2008 forward, with full recipient and sub-agency attribution.
| ๐ฏ Target Audience | ๐ก Primary Use Cases |
|---|---|
| Lending and credit analysts | Track federal loan recipients |
| Journalists and researchers | Audit federal loan portfolios |
| Compliance teams | Verify recipient activity |
| Policy researchers | Quantify loan program reach |
### ๐ What the USAspending Loans Scraper does
- Pulls loan and loan guarantee awards from the USAspending search API
- Filters by recipient keyword, fiscal year, and awarding agency
- Returns recipient, agency, loan value, subsidy cost, CFDA number, and description
- Provides a direct link to each award page on USAspending.gov
> ๐ก **Why it matters:** Federal loan portfolios are public record. This scraper makes them queryable in seconds.
### ๐ฌ Full Demo (_๐ง Coming soon_)
### โ๏ธ Input
Field
Type
Description
maxItems
integer
Cap on rows (free plan: 10)
recipientSearch
string
Optional recipient keyword filter
fiscalYear
integer
Fiscal year (2008-2030)
agency
string
Awarding agency keyword filter
```json
{ "maxItems": 50 }
````
```json
{ "maxItems": 100, "recipientSearch": "university", "fiscalYear": 2024 }
```
> โ ๏ธ **Good to Know:** USAspending serves data with a delay of up to several days. Awards may be modified retroactively.
### ๐ Output
| Field | Type | Description |
|---|---|---|
| ๐ awardId | string | USAspending award identifier |
| ๐ข recipientName | string | Award recipient |
| ๐ awardingAgency | string | Top-level federal agency |
| awardingSubAgency | string | Sub-agency |
| ๐ฒ loanValue | number | Face value of the loan |
| subsidyCost | number | Estimated subsidy cost |
| cfdaNumber | string | CFDA / Assistance Listing |
| description | string | Award description |
| issuedDate | string | Date the loan was issued |
| ๐ url | string | Award detail page |
| ๐ scrapedAt | string | ISO timestamp |
### โจ Why choose this Actor
- Direct USAspending API: no scraping fragility
- Real-time data, no cached responses
- Works with Make, Zapier, Airbyte, GitHub Actions, Google Sheets
### ๐ How it compares to alternatives
| Approach | Cost | Maintenance | Coverage |
|---|---|---|---|
| This actor | Pay per result | None | Full |
| Manual USAspending download | Free | High | Limited UI |
| Commercial federal-loan databases | $$$ | None | Curated |
### ๐ How to use
1. [Create a free account w/ $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp)
2. Open the actor page
3. Set inputs
4. Run
5. Download dataset as CSV, Excel, JSON, or XML
### ๐ผ Business use cases
#### Competitor and market monitoring
Watch which lenders or recipients are receiving federal loan guarantees in your sector.
#### Pipeline expansion
Identify federal agencies actively issuing loans in adjacent industries.
#### Compliance and due diligence
Validate that prospects received federal loans before doing diligence.
#### Market sizing and research
Aggregate loan value per program or fiscal year for portfolio analysis.
### ๐ Automating USAspending Loans Scraper
Connects with Make, Zapier, Slack, Airbyte, GitHub Actions, and Google Drive via Apify's standard integrations.
### ๐ Beyond business use cases
#### Research
Academic studies on federal credit programs and subsidy cost.
#### Personal
Curious citizens tracking how loan guarantees are awarded.
#### Non-profit
Watchdog organizations auditing loan recipient categories.
#### Experimentation
Train models on labelled federal loan data.
### ๐ค Ask an AI assistant about this scraper
ChatGPT, Claude, Perplexity, or Copilot can explain inputs, sample outputs, and integration patterns.
### โ Frequently Asked Questions
**Q: Is the data real-time?** A: USAspending publishes with a several-day lag.
**Q: Are SBA 7(a) and 504 loans included?** A: Yes, all federal loan programs (codes 07 and 08).
**Q: How far back does data go?** A: FY2008 via the search API.
**Q: Is recipient name standardized?** A: It is the recipient name as reported.
**Q: Can I filter by CFDA?** A: Use the recipient or agency filters. CFDA appears in output.
**Q: Are dollar amounts inflation-adjusted?** A: No, nominal dollars.
**Q: Does this include classified programs?** A: No, only publicly disclosed records.
**Q: Are sub-awards included?** A: This actor returns prime awards.
**Q: How accurate is the data?** A: As accurate as the USAspending source data.
**Q: Can I get loans by state?** A: Use recipient or agency filters; state filtering is a future enhancement.
### ๐ Integrate with any app
Make, Zapier, Slack, Airbyte, GitHub Actions, Google Sheets, Webhooks.
### ๐ Recommended Actors
| Actor | Description |
|---|---|
| [USAspending Contracts Scraper](https://apify.com/parseforge/usaspending-contracts-scraper) | Federal contract awards |
| [FDIC Bank Scraper](https://apify.com/parseforge/fdic-bank-scraper) | FDIC institution data |
| [SEC EDGAR Scraper](https://apify.com/parseforge/sec-edgar-scraper) | SEC company filings |
> ๐ก **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge).
**๐ Need Help?** [Open our contact form](https://tally.so/r/BzdKgA)
> **โ ๏ธ Disclaimer:** independent tool, not affiliated with the source organization. Only publicly available data collected.
# Actor input Schema
## `maxItems` (type: `integer`):
Free users: 10. Paid: up to 1,000,000
## `recipientSearch` (type: `string`):
Filter by recipient name keyword
## `fiscalYear` (type: `integer`):
Fiscal year (2008-2030)
## `agency` (type: `string`):
Top-level agency keyword filter
## Actor input object example
```json
{
"maxItems": 10
}
```
# 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 = {
"maxItems": 10
};
// Run the Actor and wait for it to finish
const run = await client.actor("parseforge/usaspending-loans-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`๐พ 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);
});
// ๐ Want to learn more ๐? Go to โ 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 = { "maxItems": 10 }
# Run the Actor and wait for it to finish
run = client.actor("parseforge/usaspending-loans-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("๐พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"maxItems": 10
}' |
apify call parseforge/usaspending-loans-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=parseforge/usaspending-loans-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "USAspending Loans Scraper | Federal Loan Programs Data",
"description": "Export federal loan and loan guarantee awards from USAspending.gov: recipient, awarding agency, loan value, subsidy cost, CFDA number, issued date and description. Filter by recipient name, fiscal year and agency. CSV, Excel, JSON or XML for credit research, compliance and policy work.",
"version": "0.1",
"x-build-id": "DlbFiDX86P2iaKTxA"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/parseforge~usaspending-loans-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-parseforge-usaspending-loans-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/parseforge~usaspending-loans-scraper/runs": {
"post": {
"operationId": "runs-sync-parseforge-usaspending-loans-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/parseforge~usaspending-loans-scraper/run-sync": {
"post": {
"operationId": "run-sync-parseforge-usaspending-loans-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"properties": {
"maxItems": {
"title": "Max Items",
"minimum": 1,
"maximum": 1000000,
"type": "integer",
"description": "Free users: 10. Paid: up to 1,000,000"
},
"recipientSearch": {
"title": "Recipient Search",
"type": "string",
"description": "Filter by recipient name keyword"
},
"fiscalYear": {
"title": "Fiscal Year",
"minimum": 2008,
"maximum": 2030,
"type": "integer",
"description": "Fiscal year (2008-2030)"
},
"agency": {
"title": "Awarding Agency (optional)",
"type": "string",
"description": "Top-level agency keyword filter"
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```