Success Story
Optimizing Fraud Detection and ETL Pipelines for Insurer Operations
Qantev
February 2025
Developed a backend system for exclusion rule enforcement and optimized ETL pipelines, significantly enhancing fraud detection accuracy and data reliability for a health-tech insurer.
Fraud Prevention
Protecting revenue through intelligent detection
Technologies Used
Azure
Dagster
Django
Python
Terraform
GitHub
The Challenge
- Manual validation efforts and inefficient claim processing due to complex insurer-specific exclusion rules.
- Need to improve the accuracy and reliability of the fraud detection model.
- Existing ETL pipelines required optimization for scalability and accessibility.
- Legacy codebase needed refactoring to improve maintainability and performance.
The Solution
- Developed and deployed a backend system that automatically identifies non-covered treatments by enforcing insurer-specific exclusion rules.
- Designed and implemented scalable ETL pipelines using Dagster to extract, transform, and integrate customer data.
- Optimized the fraud detection model, enhancing its performance and reducing false positives.
- Refactored critical components of the legacy codebase, modernizing data processing.
Results & Impact
- Reduced manual validation efforts and improved claim processing efficiency.
- Significantly reduced false positives in fraud detection, increasing accuracy and trust in the system, minimizing financial risk.
- Improved data reliability and accessibility for analytics teams through optimized ETL pipelines.
- Enhanced maintainability, scalability, and execution performance of data processing components.
Project Summary
ClientQantev
CompletedFebruary 2025
CategoryData Engineering
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