Development of a Data Quality Monitoring and Risk Analytics Platform for Financial Risk Management

Financial organizations are required by regulators, stakeholders, and auditors to provide verifiable evidence and proof of work to justify their pricing and positions. They face complexities due to inadequate contextual analysis, limited timeseries analytics, and insufficient auditable documentation. These challenges hinder transparent decision-making, hinder real-time identification of data issues, and impede rapid rootcause analysis for high-impact risks.

A mid-to-large financial institution, such as a bank or asset management firm, seeking to enhance its data integrity, risk analysis, and regulatory compliance through an integrated analytics platform.

• Build a scalable platform that ensures data integrity and reliability across large financial datasets.
• Implement real-time data quality monitoring with exception detection and rootcause analysis capabilities.
• Create an intuitive, zero-code user interface for monitoring, analysis, and report generation to enable non-technical users.
• Facilitate comprehensive scenario and trend analysis, including price backtesting and contextual reviews.
• Support seamless integration with existing data sources, analytics tools, and regulatory reporting systems.
• Enhance transparency and auditable documentation to support compliance and informed decision-making.

• Real-time data quality monitoring with dashboards for exception alerts.
• Automated rootcause analysis tools to identify underlying issues swiftly.
• Scenario analysis, trend visualization, and backtesting modules for asset valuation.
• Contextual review capabilities integrating multiple data sources and workflows.
• Self-serve dataset creation and management with complex data lineage visualization.
• Zero-code interface enabling users to perform monitoring and analysis without extensive coding skills.
• Automated and auditable documentation generation for compliance purposes.

• Data sources for internal and external datasets.
• Existing data management and analytics platforms.
• Regulatory reporting systems.
• Workflow management tools for contextual analytics.

• High scalability to accommodate growing datasets and user base.
• Real-time processing capabilities for immediate detection and resolution of data issues.
• Security features aligning with financial industry compliance standards.
• Performance metrics ensuring sub-second response times for data quality dashboards.
• Reliability and availability to ensure continuous monitoring without interruptions.

The platform aims to enable financial organizations to navigate complex decision-making processes more effectively by integrating comprehensive data quality management, real-time risk analytics, and detailed audit documentation. Expected outcomes include improved data reliability, faster rootcause resolution, enhanced regulatory compliance, and more accurate asset valuation insights, ultimately supporting informed and confident financial decisions.