In this project, I worked on automating the process of mapping external credit ratings to standardized internal risk weights for the Office of the Superintendent of Financial Institutions (OSFI). The project involved several key steps:
Data Cleaning: Created new tables to store unique CUSIP identifiers, ensuring that all subsequent analyses were performed on valid and unique data points.
Conversion of Ratings: Translated institutional credit ratings from multiple agencies into internal risk weights using a series of LEFT JOIN operations. This included handling complex challenges such as counting valid ratings per bond using CASE statements.
Merging and Sorting Data: Combined data from multiple sources using UNION operations and sorted it to organize the internal grades effectively. This ensured that the data was neatly structured for further analysis.
Application of OSFI Rules: Applied specific rules to determine the standardized internal grades for each bond, based on the number and diversity of external ratings. This step was crucial in aligning the risk assessments with regulatory requirements.
Final Data Integration: Merged the derived internal grades back into the original dataset, enabling a comprehensive risk assessment that aligns with both internal and external standards.
External Rating Conversion: Converted the internal grades back into an external rating format, ensuring consistency with external benchmarks and regulatory guidelines.
This project required a deep understanding of financial data management, regulatory compliance, and automation techniques. The enhancements I implemented resulted in a more accurate and efficient process for assessing credit risk, supporting OSFI’s regulatory oversight functions.