-
500+
Market indicators & time series data utilized
-
1
Centralized repository for firm-wide scenario definitions
-
40%
Reduced resource cost
CLIENT CHALLENGES
- Meeting various regulatory requirements
- Lack of expertise in model validations and stress scenarios
- Inefficient governance to execute scenario expansion
OUR APPROACH
- Implement R models adapting on Merton’s credit risk model algorithm for various scenarios and regulatory frameworks
- Develop aggregation models to chain model run results
- Build DQ controls to check for accuracy and completeness
- 2 data scientists implemented these models in R and deployed in a ETL framework based on Python microservices
IMPACT DELIVERED
- Developed a scalable solution considering existing infrastructure and models.
- Robust solution with high quality output, driven by subject matter experts and rigorous quality check process.
- Deployed highly qualified and experienced resources at several client locations in fraction of the cost
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