Business Situation
- The client’s exception management workflow was highly dependent on manual intervention to configure machine learning (ML) for automated data extraction.
- Due to inconsistent extraction requirements, the process lacked efficiency, resulting in an average document processing time of 10 minutes.
- As data volumes grew and client onboarding accelerated, the existing approach became increasingly unsustainable, necessitating a more scalable and automated solution.
SGA Approach
Technology
- Daily summary reports, workflow monitoring tools, and automated summaries helped optimize performance and track progress.
- Periodic reports and automated workflows reduced human intervention and further optimized data extraction processes.
AI
- AI/ML models were trained to improve extraction accuracy, boosting performance from 55% to 80%.
- SGA partnered with the client to create a unified data extraction strategy, reducing human effort and enabling system-wide automation.
Data
- Extraction rates, processing times, and project turnaround times were tracked to ensure continuous improvement.
- Models were trained on diverse document types and exceptions, improving extraction accuracy from 55% to 80% over time and reducing manual intervention.
Key Takeaways
- Efficiency Boost: Cycle time was reduced by 70%, speeding up processes significantly.
- Improved Accuracy: Machine learning model accuracy increased from 55% to 80%.
- Scalability and Performance: The workflow was optimized for efficient handling of large volumes of data.
- Ongoing Enhancements: Continuous improvements were driven by automation and data insights.
- AI/ML-Driven Transformation: The solution provided enhanced agility, reduced human effort, and accelerated data delivery through automation.
