Banking — Financial Data Quality Normalization Pipeline

Overview

The Financial Data Quality Normalization Pipeline is designed to ensure the integrity and compliance of financial data within the banking industry. Its primary purpose is to normalize ingested financial data and apply stringent quality tests to verify adherence to predefined standards. The pipeline begins with the ingestion of various data sources, including ERP transaction logs, customer account records, and regulatory compliance reports. Each data source is processed through a series of transf

The Financial Data Quality Normalization Pipeline is designed to ensure the integrity and compliance of financial data within the banking industry. Its primary purpose is to normalize ingested financial data and apply stringent quality tests to verify adherence to predefined standards. The pipeline begins with the ingestion of various data sources, including ERP transaction logs, customer account records, and regulatory compliance reports. Each data source is processed through a series of transformation steps that standardize formats, cleanse anomalies, and enrich the data for better usability. Quality controls are implemented at each stage using defined expectations to validate the data against compliance benchmarks. Validated data is then cataloged for traceability, ensuring that all financial records can be tracked and audited effectively. Key performance indicators (KPIs) monitored throughout the process include the percentage of compliant data and the time taken for validation. In the event of validation failures, the system generates alerts to facilitate prompt intervention. This robust approach not only enhances data quality but also significantly reduces compliance risks, ultimately delivering greater business value by ensuring reliable financial reporting and decision-making.

Part of the Governance & Compliance solution for the Banking industry.

Use cases

• Improves data integrity for accurate financial reporting
• Reduces compliance risks through stringent validation
• Enhances operational efficiency with automated processes
• Facilitates better decision-making with reliable data
• Strengthens regulatory compliance and governance frameworks