Banking — Corpus Normalization and Enrichment for Enhanced Analysis

Overview

The purpose of the finance_km1_corpus_normalisation DAG is to standardize and enrich financial data corpora, facilitating enhanced analysis within the banking sector. The pipeline begins with the ingestion of various data sources, including transaction logs, customer records, and regulatory reports. These inputs are processed through a series of steps that include data normalization according to predefined standards, application of quality rules to ensure data integrity, and generation of metada

The purpose of the finance_km1_corpus_normalisation DAG is to standardize and enrich financial data corpora, facilitating enhanced analysis within the banking sector. The pipeline begins with the ingestion of various data sources, including transaction logs, customer records, and regulatory reports. These inputs are processed through a series of steps that include data normalization according to predefined standards, application of quality rules to ensure data integrity, and generation of metadata for tracking and traceability. The normalization process transforms raw data into a consistent format, while quality checks validate the accuracy and completeness of the data. In the event of any discrepancies or errors detected during processing, alerts are sent to responsible personnel for immediate action. The final output consists of a set of normalized and enriched datasets ready for advanced analytics, which can drive insights and decision-making. Monitoring key performance indicators (KPIs) such as data accuracy rates, processing times, and error rates ensures the ongoing effectiveness of the DAG. The business value of this DAG lies in its ability to provide reliable, high-quality data for analysis, ultimately supporting better financial decision-making and compliance with regulatory requirements.

Part of the Knowledge Portal & Ontologies solution for the Banking industry.

Use cases

• Enhances data quality for accurate financial analysis
• Supports regulatory compliance and reporting requirements
• Improves decision-making through reliable data insights
• Reduces operational risks associated with data errors
• Increases efficiency in data processing and analysis