Banking — Model Lineage Tracking for Governance

Overview

The primary purpose of this DAG is to facilitate the traceability of models utilized within banking decision-making processes, thereby enhancing governance and compliance. The data sources for this workflow include model management systems and relational databases that store model metadata and performance metrics. The ingestion pipeline begins with the extraction of model version data, followed by impact analysis to assess how changes in models affect decision outcomes. This is followed by the g

The primary purpose of this DAG is to facilitate the traceability of models utilized within banking decision-making processes, thereby enhancing governance and compliance. The data sources for this workflow include model management systems and relational databases that store model metadata and performance metrics. The ingestion pipeline begins with the extraction of model version data, followed by impact analysis to assess how changes in models affect decision outcomes. This is followed by the generation of compliance reports that document model usage and performance against regulatory standards. Quality controls are integrated at each step to ensure data integrity, including validation checks on model versions and impact assessments. In the event of any discrepancies or failures in the processing steps, a notification mechanism is triggered to alert responsible stakeholders, ensuring timely intervention. The outputs of this DAG include detailed compliance reports, version control logs, and impact assessment summaries. Monitoring key performance indicators (KPIs) such as model performance metrics and compliance adherence rates provides insights into the effectiveness of the governance framework. The business value of this DAG lies in its ability to enhance transparency in model usage, mitigate compliance risks, and support informed decision-making processes in the banking sector.

Part of the Enterprise Search solution for the Banking industry.

Use cases

• Enhances regulatory compliance and reduces risk exposure.
• Improves decision-making transparency and accountability.
• Facilitates efficient model management and governance.
• Supports timely interventions through automated alerts.
• Increases stakeholder confidence in model reliability.