Banking — Taxonomy and Entity Extraction for Knowledge Structuring

Overview

The purpose of this DAG is to systematically extract entities and taxonomies from standardized data sources within the banking industry, thereby structuring data for improved accessibility and searchability. The data sources include normalized corpora, such as transaction records and customer profiles, which are ingested into the workflow. The ingestion pipeline begins with data extraction, followed by the application of information extraction techniques to identify and categorize key concepts r

The purpose of this DAG is to systematically extract entities and taxonomies from standardized data sources within the banking industry, thereby structuring data for improved accessibility and searchability. The data sources include normalized corpora, such as transaction records and customer profiles, which are ingested into the workflow. The ingestion pipeline begins with data extraction, followed by the application of information extraction techniques to identify and categorize key concepts relevant to banking operations. Each identified entity is then mapped to a knowledge graph, facilitating easier access and enhanced search capabilities for users. Quality control measures are implemented throughout the process to ensure the accuracy of the extracted data, including automated validation checks and manual review processes in case of discrepancies. The outputs of this DAG include a well-structured knowledge graph, detailed reports on extracted entities, and taxonomy classifications that can be utilized for further analysis. Monitoring key performance indicators (KPIs) such as extraction accuracy, processing time, and user engagement with the knowledge portal is crucial for assessing the effectiveness of the workflow. The business value lies in improved data organization, enhanced decision-making capabilities, and increased operational efficiency within the banking sector.

Part of the Knowledge Portal & Ontologies solution for the Banking industry.

Use cases

• Improved data organization enhances decision-making processes
• Increased efficiency in data retrieval and analysis
• Higher accuracy in entity recognition reduces operational risks
• Facilitates compliance with regulatory data requirements
• Strengthens customer insights through structured data access