Banking — Financial Data Quality Validation and Normalization Pipeline

Overview

The primary purpose of the finance_km2_data_quality DAG is to validate and normalize financial data ingested from various sources, ensuring that it meets the required quality standards for pricing optimization in the banking sector. The data sources include ERP transaction logs, customer financial profiles, and market pricing data. The ingestion pipeline begins with the collection of these datasets, followed by a series of processing steps designed to assess data quality. Initially, the data und

The primary purpose of the finance_km2_data_quality DAG is to validate and normalize financial data ingested from various sources, ensuring that it meets the required quality standards for pricing optimization in the banking sector. The data sources include ERP transaction logs, customer financial profiles, and market pricing data. The ingestion pipeline begins with the collection of these datasets, followed by a series of processing steps designed to assess data quality. Initially, the data undergoes validation checks, where it is compared against predefined quality metrics to identify any discrepancies or anomalies. Next, normalization processes are applied to standardize the data formats, ensuring consistency across datasets. Quality control measures are implemented, including automated tests that trigger alerts in case of failures, allowing for rapid remediation. The processed data is then cataloged to enhance traceability and governance, ensuring compliance with regulatory standards. Outputs of this DAG include validated and normalized financial datasets, comprehensive quality reports, and alert notifications for any data quality issues. Monitoring key performance indicators (KPIs) such as data accuracy, completeness, and timeliness is crucial for maintaining data integrity. The business value derived from this DAG lies in its ability to provide reliable data for pricing strategies, ultimately leading to better decision-making and improved customer satisfaction in the banking industry.

Part of the Pricing Optimization solution for the Banking industry.

Use cases

• Improved reliability of financial data for decision-making
• Enhanced compliance with regulatory standards
• Increased operational efficiency through automated processes
• Faster response times to data quality issues
• Better customer satisfaction through accurate pricing strategies