Banking — Knowledge Graph Creation for Enhanced Information Retrieval

Overview

The primary purpose of this DAG is to create knowledge graphs that enhance information retrieval within the banking sector. It ingests data from multiple sources, including transaction records, customer feedback, and market analysis reports, which are both structured and unstructured. The ingestion pipeline begins with data extraction from these diverse sources, followed by data cleaning and transformation to ensure consistency and quality. The processed data is then integrated into a graph mana

The primary purpose of this DAG is to create knowledge graphs that enhance information retrieval within the banking sector. It ingests data from multiple sources, including transaction records, customer feedback, and market analysis reports, which are both structured and unstructured. The ingestion pipeline begins with data extraction from these diverse sources, followed by data cleaning and transformation to ensure consistency and quality. The processed data is then integrated into a graph management system, where relationships and entities are defined to facilitate advanced querying and analysis. A robust quality control mechanism is in place to monitor data integrity and accuracy throughout the process. In the event of a failure, a recovery mechanism is activated to maintain workflow continuity, ensuring that the system remains operational and reliable. The outputs of this DAG include enriched knowledge graphs that can be utilized for advanced analytics and decision-making. Key performance indicators (KPIs) for monitoring include data processing time, error rates, and the accuracy of graph relationships. The business value of this DAG lies in its ability to significantly enhance information retrieval capabilities, leading to improved customer insights, faster decision-making, and ultimately, increased operational efficiency in banking operations.

Part of the Predictive Maintenance solution for the Banking industry.

Use cases

• Improves customer insights through enhanced data relationships.
• Increases operational efficiency by streamlining information retrieval.
• Supports faster decision-making with enriched analytics capabilities.
• Reduces downtime with effective recovery mechanisms.
• Enhances compliance and reporting accuracy through structured data.