Transport & Logistics — Named Entity Extraction from Transport Documents

Overview

The purpose of this DAG is to extract named entities from transport documents, such as contracts and invoices, to improve search efficiency and support fraud detection within the transport and logistics industry. The data sources include document management systems and internal databases, which serve as the foundational inputs for the extraction process. The ingestion pipeline begins with the collection of relevant documents, followed by the application of advanced natural language processing (N

The purpose of this DAG is to extract named entities from transport documents, such as contracts and invoices, to improve search efficiency and support fraud detection within the transport and logistics industry. The data sources include document management systems and internal databases, which serve as the foundational inputs for the extraction process. The ingestion pipeline begins with the collection of relevant documents, followed by the application of advanced natural language processing (NLP) models that facilitate the extraction of pertinent entities. Once the data is extracted, normalization processes are employed to standardize the information, ensuring consistency across various data formats. Quality control measures are integral to this workflow, incorporating checks for accuracy and compliance with data security standards to mitigate risks associated with data handling. The extracted entities are then stored in a centralized data warehouse, making them readily accessible for future analysis and reporting. Key performance indicators (KPIs) monitored throughout this process include extraction accuracy rates and processing times, providing insights into the efficiency and effectiveness of the DAG. In the event of a failure, a robust recovery mechanism is in place to automatically restart the extraction process, minimizing downtime and ensuring continuous operation. This DAG not only enhances operational efficiency but also significantly contributes to the organization's ability to detect and prevent fraudulent activities.

Part of the Fraud & Anomaly Analytics solution for the Transport & Logistics industry.

Use cases

• Improved search capabilities for transport documentation
• Enhanced fraud detection through accurate data extraction
• Streamlined operations with automated workflows
• Increased data accuracy leading to better decision-making
• Compliance with data security standards ensuring risk mitigation