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Application Testing and Migration: Automating Credit Risk Summary Reporting by Going from Excel-Based Processes to a Cloud-Based Solution

Overview

Our application testing team helped a leading bank in the US to automate the preparation of Credit Risk Summary reports. Experienced application testing engineers helped transition from Excel-based processes to a cloud-based solution. The implementation leveraged our expertise in QlikSense for data visualization, Unqork for the user interface, and Snowflake and Matillion for seamless data integration and storage.

Our application modernization services helped automate the Public Member Credit Summary Report and create a dynamic dashboard that displays historical equity data. This dashboard will support both daily and ad-hoc reporting, while populating a newly developed Snowflake database for efficient data management and analysis.

Solution Overview

  • Seamless Cloud Migration: Migrated the bank’s Credit Risk Summary reports from Excel-based processes to a cloud-based solution using Snowflake, QlikSense, and Unqork.
  • End-to-End Automation: Automated data validation, risk rating identification, and financial reporting, reducing manual effort and improving accuracy.
  • Real-Time Data Integration: Implemented real-time financial data acquisition, ensuring timely insights for decision-making.
  • Enhanced User Experience: Delivered an intuitive Unqork interface for managing and adjusting risk ratings, improving user accessibility.
  • Improved Reporting Capabilities: Optimized Public Member Credit Summary reports through enhanced data visualization and automated reporting with QlikSense.
  • Efficient Risk Assessment: Integrated multiple risk indicators into a centralized risk assessment platform, streamlining risk analysis.
  • Compliance-Driven Solution: Strengthened compliance by automating data validation and risk assessment processes using Snowflake and Matillion.
  • Bloomberg and Moody’s Integration: Seamlessly integrated Bloomberg Ticker Symbols and Moody’s Credit Edge data, ensuring comprehensive risk analysis.
  • Data Integrity Assurance: Implemented data mapping solutions to ensure clean, consistent data and avoid duplication.
  • Scalability and Maintenance: Developed a scalable maintenance solution for handling data without a source, ensuring long-term system stability.

Challenge:

  • Manual and Fragmented Process: The process was highly manual, with no historical data view except for individual excel reports.
  • High Licensing and Access Costs: Producing reports requires each user to have a BB Anywhere license, in addition to access to the Moody’s Credit Edge model.
  • Tedious Data Acquisition: Gathering data from various sources using APIs is a time-consuming task for stakeholders.
  • Data Retrieval Challenges: Retrieving up-to-date data for indexes based on Bloomberg Tickers is complex and inefficient.
  • Limited Historical Data Access: Accessing and storing historical data for Public Members and related indexes is difficult, creating gaps in reporting.
  • Cumbersome ETL Process: Setting up an ETL process to handle end-of-day data every workday and store it in Snowflake is resource-intensive.
  • Integration of Internal Data Sources: Identifying and incorporating internal data sources like Advances and Collateral for Public Members is complex.
  • Inconsistent Data and Calculation Logic: Complex calculation logic in the internal scorecard leads to inconsistent data and compromises report integrity.

How We Helped

Robust Data Validation Framework:

Implemented a comprehensive Snowflake data validation framework.

It significantly enhanced risk assessment accuracy.

 

Intuitive User Interface:

Designed and deployed a user-friendly search and editable fields.

Utilized Unqork’s Management Adjustment UI for improved usability.

 

Automated Risk Rating Identification:

Automated CAMELS Risk Rating identification.

We used Snowflake and Matillion for improving efficiency.

 

Centralized Risk Assessment Platform:

Integrated various risk indicators into Qlik Sense platform.

Gives easily drill down into data points to investigate further risk.

 

Efficient Risk Rating Adjustments

Streamlined risk-rating adjustments by leveraging Snowflake’s AI processing.

Catch trends and patterns that human analysts would be unlikely to spot.

 

Real-Time Data Acquisition:

Developed a real-time financial data acquisition process.

Used Matillion and Unqork for up-to-date insights.

 

Enhanced Compliance

Automated data validation and risk assessment

Workflows set up in Snowflake and Matillion.

 

User-Friendly Reporting Interface

New UI eliminated toggling between different legacy systems.

Eliminated manual data entry from one user platform to another.

 

Bloomberg and CDS Integration with QlikSense

Optimized Public Member Credit Summary UI and reporting.

Integrated Bloomberg Ticker Symbols and CDS Data into QlikSense.

 

Automated Data Acquisition

Automated acquisition of credit edge data from Moody’s.

Done via Snowflake to ensure real-time accuracy.

 

Data Mapping & Duplication Prevention

Implemented data mapping to prevent duplication.

Ensured consistency across the platform.

 

Data Maintenance Solution

Developed a scalable maintenance table solution to handle data.

Ensured data handling without a source, thereby improving data integrity.

 

Tidal Jobs for Data Acquisition

Developed and scheduled Tidal jobs for acquiring data from various sources.

Built Matillion pipelines with sources such as Credit Edge and Bloomberg to automate the process.

 

Data Storage with Snowflake

Streamlined, processed, and calculated data stored in Snowflake.

Utilized Slowly Changing Dimensions (SCD) and dynamic tables wherever necessary.

 

Job Failure Logging and Analysis

Implemented logging for all data acquisition and transformation jobs.

Whenever a failure occurs, it ensures prompt issue resolution and reliability.

Benefits Unlocked

  • Seamless Cloud Migration: Migrated the bank’s Credit Risk Summary reports from Excel-based processes to a cloud-based solution using Snowflake, QlikSense, and Unqork.
  • End-to-End Automation: Automated data validation, risk rating identification, and financial reporting, reducing manual effort and improving accuracy.
  • Real-Time Data Integration: Implemented real-time financial data acquisition, ensuring timely insights for decision-making.
  • Enhanced User Experience: Delivered an intuitive Unqork interface for managing and adjusting risk ratings, improving user accessibility.
  • Improved Reporting Capabilities: Optimized Public Member Credit Summary reports through enhanced data visualization and automated reporting with QlikSense.
  • Efficient Risk Assessment: Integrated multiple risk indicators into a centralized risk assessment platform, streamlining risk analysis.
  • Compliance-Driven Solution: Strengthened compliance by automating data validation and risk assessment processes using Snowflake and Matillion.
  • Bloomberg and Moody’s Integration: Seamlessly integrated Bloomberg Ticker Symbols and Moody’s Credit Edge data, ensuring comprehensive risk analysis.
Contract Driven Development

 

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