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Power BI Credit Rating Analytics & Data Transformation for Agencies
Problem Statement: Inefficient Data Management Hinders Credit Rating Analysis
The agency faced major obstacles in delivering timely, high-quality credit ratings due to poor data transformation workflows. The use of Power BI for both reporting and data consolidation was ineffective, and critical operations were delayed by manual data matching across databases such as Snowflake and Ratings DB. Missing keys, fragmented sources, and inconsistent data further exacerbated the issue.
Key pain points included:
• Disconnected systems and manual data reconciliation
• Data quality issues affecting credit rating accuracy
• Inability to scale for growing financial data volumes
• Delayed and error-prone reporting cycles
Impact: Manual Processes & Poor Data Visibility
These inefficiencies led to:
High operational costs from manual interventions
Frequent data mismatches undermining report integrity
Delayed risk assessment due to fragmented financial insights
Bottlenecks in producing regulatory and analytical credit reports
Solution: Data Transformation for Credit Rating Agency Using Power BI
JBS deployed a comprehensive data transformation and analytics solution tailored for the credit rating sector:
1. Centralized Data Platform
Unified disparate financial and sovereign data into a single source of truth, improving visibility and reducing inconsistencies.
Unified disparate financial and sovereign data into a single source of truth, improving visibility and reducing inconsistencies.
2. Automated Data Preparation
Embedded quality rules and business logic into automated ingestion flows, drastically improving data quality and reliability.
Embedded quality rules and business logic into automated ingestion flows, drastically improving data quality and reliability.
3. Scalable Data Model
Created a modular and reusable data warehouse model, integrated with Snowflake and Power BI for agility and scale.
Created a modular and reusable data warehouse model, integrated with Snowflake and Power BI for agility and scale.
4. Power BI Credit Rating Analytics
Built dynamic Power BI dashboards tailored for analysts, enabling visualization of risk scores, entity history, and market dynamics.
Built dynamic Power BI dashboards tailored for analysts, enabling visualization of risk scores, entity history, and market dynamics.
5. Self-Service BI for Credit Risk Assessment
Empowered users to create their own reports without IT involvement, democratizing access to complex financial data.
Empowered users to create their own reports without IT involvement, democratizing access to complex financial data.
6. AI/ML Analytics Readiness
Established infrastructure to integrate AI/ML tools for predictive credit risk modeling in future phases.
Established infrastructure to integrate AI/ML tools for predictive credit risk modeling in future phases.
Business Benefits: Data-Driven Credit Rating Decisions
The new system delivered measurable outcomes:
95% Reduction in Data Errors
Clean data improved confidence in risk evaluations
Clean data improved confidence in risk evaluations
30-40% Faster Data Development
Reduced reporting time and model update cycles
Reduced reporting time and model update cycles
70% Drop in Manual Reconciliation:
Operations streamlined through automation
Operations streamlined through automation
25% Increase in Analyst Productivity
Easier access to reliable data through BI tools.
Easier access to reliable data through BI tools.
50% Fewer IT Requests
Widespread self-service reporting adoption
Widespread self-service reporting adoption
AI/ML Integration Ready
Positioned for next-gen credit scoring and modeling
Positioned for next-gen credit scoring and modeling
Technology Stack
ADLS Gen2
Azure Synapse Spark
Snowflake
Power BI
PowerApps
Azure Data Factory
AI/ML Models (future-ready)
Let's Get in Touch
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Karnataka – 560043
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Telangana – 500081
Telangana – 500081
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