Banking — Financial Data Extraction and Structuring Pipeline

Overview

The purpose of this DAG is to extract critical information from financial datasets and organize it according to a predefined taxonomy. It leverages Named Entity Recognition (NER) techniques to identify relevant entities, ensuring accurate classification of financial data. The data sources include transaction logs, financial statements, and regulatory filings, which are ingested into the pipeline for processing. The ingestion pipeline consists of several steps: first, data is extracted from the s

The purpose of this DAG is to extract critical information from financial datasets and organize it according to a predefined taxonomy. It leverages Named Entity Recognition (NER) techniques to identify relevant entities, ensuring accurate classification of financial data. The data sources include transaction logs, financial statements, and regulatory filings, which are ingested into the pipeline for processing. The ingestion pipeline consists of several steps: first, data is extracted from the specified sources; next, the NER process identifies and categorizes entities; subsequently, the structured data is stored in a graph database for efficient retrieval. Quality controls are implemented to monitor the extraction success rate and processing time, with key performance indicators (KPIs) such as successful extraction rates and average processing duration being tracked. In the event of extraction failures, a retry mechanism is activated to ensure data integrity and completeness. The outputs of this DAG include a structured graph of financial entities, reports on extraction performance, and an updated data catalog. This structured approach not only enhances data accessibility but also supports compliance with regulatory requirements, ultimately driving business value by improving decision-making processes and operational efficiency in the banking sector.

Part of the Data & Model Catalog solution for the Banking industry.

Use cases

• Improves data discoverability for financial analysts
• Enhances compliance with regulatory requirements
• Increases operational efficiency through automated processes
• Reduces time spent on manual data categorization
• Supports informed decision-making with structured data