Case Study · ML Ops
Big-Data Python ETL Tool
Airvantage processes billions of airtime loans for the biggest Mobile Network Operators in Africa and Latin America, every year. Getting that volume of data from point A to point B is easier said than done.
The Challenge
Billions of Loans. One Fragile Pipeline.
Airvantage’s decision science determines which users get provisioned a mobile loan – a judgment call made billions of times a year.
That decision science runs across some of the largest Mobile Network Operators in Africa and Latin America. The data behind those decisions needed to move reliably from source systems into the models and dashboards that depend on it.
At that volume, a fragile pipeline isn’t a minor inconvenience. It’s a business risk, and Airvantage needed both a clear-eyed assessment of their existing decision science and a pipeline built to match the scale of the problem.
- Billions of loan records
- Multi-region MNO data
- Legacy ETL bottlenecks
- Decision science auditing
- Downstream ML dependencies
Challenge
Solutions Built
Assess First. Then Build the Pipe.
Symbyte paired a full assessment of Airvantage’s credit decision science with a Python ETL tool built to move billions of rows reliably.
Machine Learning Assessment
A full assessment of Airvantage’s decision science determining which users receive a mobile loan, delivering a detailed report on possible improvements and the current state of their credit models.
Python ETL Tool
Built on cloud computing and a Python CLI tool, ensuring Airvantage’s billions of rows of loan data land in the right place, at the right time, for both ML models and downstream analysis.
Billions of Rows, Reliably Routed
The ETL pipeline moves Airvantage's full data volume where it needs to go, on schedule, every time.
A Clear Picture of Credit Model Health
The assessment gave Airvantage a documented view of their decision science, with concrete improvement paths.
Built for Multi-Region Scale
The pipeline was designed to serve the biggest Mobile Network Operators across Africa and Latin America simultaneously.
Faster Path from Data to Decision
Downstream ML models and analysis now run against dependable, well-routed data instead of working around pipeline gaps.
