extract swifts
This workflow automatically retrieves SWIFT codes and related bank information from countries around the world, supporting pagination and batch processing. By cleaning and standardizing the data, it stores the information in a MongoDB database, ensuring data integrity and real-time updates. This process significantly simplifies the cumbersome steps of manually obtaining and organizing SWIFT codes, providing financial institutions, technology companies, and data analysts with an efficient and accurate international bank code database that supports cross-border transfers, risk control checks, and data analysis needs.
Tags
Workflow Name
extract_swifts
Key Features and Highlights
This workflow automatically scrapes SWIFT codes and related bank information from countries worldwide via the website "https://www.theswiftcodes.com/browse-by-country/". It supports pagination handling and batch processing. After data cleansing and normalization—leveraging the uProc Geographic Information API—the structured data is stored in a MongoDB database, facilitating subsequent querying and analysis. Its highlights include fully automated scraping, data normalization, breakpoint resume capability, and incremental updates, ensuring data completeness and real-time accuracy.
Core Problems Addressed
- Manual collection and organization of SWIFT codes across countries is tedious and error-prone
- Complex pagination on the source website makes complete data extraction difficult
- Inconsistent data formats hinder direct utilization
- Need for structured data storage to enable fast querying and analysis
This workflow achieves efficient and accurate acquisition and storage of SWIFT code data through automation, pagination management, and data cleansing.
Application Scenarios
- Financial institutions requiring global bank SWIFT codes for cross-border transfers and risk control checks
- FinTech companies integrating SWIFT code databases when building payment or remittance platforms
- Data analysts and R&D teams conducting financial data mining and integration
- Enterprises and service providers needing frequent updates of international bank code information
Main Process Steps
- Manually trigger the workflow execution
- Create a local cache directory to prepare the data storage environment
- Send HTTP requests to retrieve the main page HTML and extract all country links
- Batch split processing by country; call the uProc API to normalize country names and codes
- Send HTTP requests based on country links to fetch corresponding page HTML (with caching and reuse support)
- Extract and parse bank name, SWIFT code, city, branch, and other information from the pages
- Detect if there is a next page and loop to fetch complete data
- Format data and generate MongoDB document structures
- Insert the structured data into the “swifts.meetup” collection in MongoDB
- Automatically proceed to the next country after completion until all countries’ data are scraped
Involved Systems or Services
- HTTP Request node: performs web page requests
- HTML Extract node: extracts target data from HTML
- uProc API: geographic information normalization service for standardizing country names and codes
- MongoDB database: stores scraped SWIFT codes and bank information
- Local file read/write: caches web page HTML to avoid redundant requests
- SplitInBatches node: batch processes the country list to enable stepwise scraping
Target Users and Usage Value
- Financial data engineers and developers: save time on data collection and improve data accuracy
- Financial institutions and payment service providers: rapidly build international bank code repositories to support business needs
- Data analysts and researchers: obtain structured foundational financial data to support analysis and modeling
- Automation operations and data collection teams: implement efficient and stable data scraping and storage workflows
In summary, this workflow provides a comprehensive, automated, and efficient solution for users who need systematic management and utilization of global bank SWIFT code data.
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