Banking — Named Entity Extraction for Customer Database Enrichment

Overview

The primary purpose of this DAG is to enrich the banking customer database by extracting named entities from various customer documents using natural language processing techniques. The data sources include customer contracts, transaction records, and customer feedback forms. The ingestion pipeline begins with the collection of these documents, which are then processed to identify and extract relevant entities such as names, addresses, and account numbers. The processing steps involve normalizat

The primary purpose of this DAG is to enrich the banking customer database by extracting named entities from various customer documents using natural language processing techniques. The data sources include customer contracts, transaction records, and customer feedback forms. The ingestion pipeline begins with the collection of these documents, which are then processed to identify and extract relevant entities such as names, addresses, and account numbers. The processing steps involve normalization of the extracted data to conform to a standard format, followed by validation checks to ensure the accuracy and completeness of the information. In the event of data processing failures, the system triggers alerts to notify the relevant personnel for immediate intervention. The outputs of this DAG include an enriched customer database that is updated with the newly extracted entities, along with detailed logs of the processing activities. Monitoring key performance indicators (KPIs) such as entity extraction accuracy, processing time, and alert frequency allows for continuous improvement of the workflow. This DAG provides significant business value by enhancing customer insights, improving data-driven decision-making, and ensuring compliance with regulatory requirements in the banking industry.

Part of the Predictive Maintenance solution for the Banking industry.

Use cases

• Improves customer insights for personalized banking services.
• Enhances regulatory compliance through accurate data management.
• Reduces manual data entry efforts, increasing operational efficiency.
• Facilitates better risk assessment and customer profiling.
• Enables data-driven decision-making for strategic initiatives.