Banking — Financial Data Normalization and Cataloging Pipeline

Overview

The Financial Data Normalization and Cataloging Pipeline is designed to enhance the quality and traceability of financial data within the banking industry. The primary purpose of this DAG is to ingest financial data from various sources, apply rigorous quality tests, and catalog the data to ensure comprehensive lineage tracking. The data sources include transaction logs from core banking systems, regulatory reporting datasets, and customer account information. The ingestion pipeline begins with

The Financial Data Normalization and Cataloging Pipeline is designed to enhance the quality and traceability of financial data within the banking industry. The primary purpose of this DAG is to ingest financial data from various sources, apply rigorous quality tests, and catalog the data to ensure comprehensive lineage tracking. The data sources include transaction logs from core banking systems, regulatory reporting datasets, and customer account information. The ingestion pipeline begins with the extraction of these data sources, followed by normalization processes that standardize the data formats and values. Quality control measures are implemented at this stage, including validation checks and error rate calculations to ensure data integrity. Following normalization, the data is cataloged, allowing for easy retrieval and traceability, which is critical for compliance and auditing purposes. Security controls are integrated throughout the process to safeguard sensitive financial information, ensuring adherence to industry regulations. The outputs of this DAG include a compliance register detailing the processed data and its lineage, along with error reports that highlight normalization issues. Key performance indicators (KPIs) monitored during this process include the normalization error rate and the time taken for data cataloging. The business value of this DAG lies in its ability to enhance data quality, streamline compliance processes, and ultimately support better decision-making within the banking sector.

Part of the Scientific ML & Discovery solution for the Banking industry.

Use cases

• Improved data quality enhances decision-making accuracy
• Streamlined compliance processes reduce regulatory risks
• Increased transparency fosters trust with stakeholders
• Enhanced data security mitigates potential breaches
• Efficient resource allocation through optimized workflows