For a leading U.S. transportation-focused FinTech company, we automated freight document classification using NLP, machine learning, and cloud orchestration to improve speed, accuracy, and fraud detection.
Challenge
The company needed a reliable solution to classify and process large volumes of freight documents for audit and fraud detection. This process, critical for verifying document accuracy and identifying potential fraud, was historically manual and time-intensive. To optimize operations, they required an automated, scalable system that could seamlessly integrate with existing technology and quickly process documents across multiple sources.
Approach
An NLP (Natural Language Processing) document process was implemented, leveraging Azure Blob Storage to securely store documents. Documents were then sent to Peruse, a third-party Machine Learning (ML) tool designed for document auditing, which classifies and makes freight documents more readable by enhancing or reorienting them. If the document is enhanced, it will then be rendered in the UI for comparison to the original document. For messaging and orchestration, Azure Service Bus was utilized to manage communication between systems, ensuring real-time updates and streamlined workflows.
Value Delivered
This automated solution streamlined the document audit and classification process, reducing manual workloads and enhancing accuracy. By integrating ML-driven document audits and fraud detection, the company increased operational efficiency and strengthened its fraud prevention capabilities.
