Banking — Named Entity Recognition for Financial Document Extraction

Overview

The primary purpose of this DAG is to facilitate the extraction of named entities from financial documents, thereby improving search efficiency and compliance within the banking sector. It ingests data from various sources, including ERP systems, CRM platforms, and document repositories. The ingestion pipeline begins with the collection of financial documents, which are then processed using Natural Language Processing (NLP) models designed specifically for entity recognition. After the initial e

The primary purpose of this DAG is to facilitate the extraction of named entities from financial documents, thereby improving search efficiency and compliance within the banking sector. It ingests data from various sources, including ERP systems, CRM platforms, and document repositories. The ingestion pipeline begins with the collection of financial documents, which are then processed using Natural Language Processing (NLP) models designed specifically for entity recognition. After the initial extraction, the data undergoes normalization to ensure consistency and usability across different systems. Quality control measures are implemented throughout the process to validate the accuracy of the extracted data, with key performance indicators (KPIs) such as precision rate being monitored closely. In the event of processing failures, a robust retry mechanism is activated to ensure that data extraction is completed successfully. The outputs of this DAG include structured datasets that can be utilized for compliance reporting, enhanced search functionalities, and improved data analytics. By automating the extraction process, this solution not only saves time and resources but also mitigates the risk of human error, ultimately delivering significant business value in terms of operational efficiency and regulatory adherence.

Part of the Literature Review solution for the Banking industry.

Use cases

• Increased efficiency in document processing and compliance checks
• Reduction in manual errors during data extraction
• Enhanced search capabilities for financial data retrieval
• Improved regulatory compliance through accurate data reporting
• Time savings leading to cost reductions in operations