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5 minutes per credit rating
Generate a credit rating in four easy steps, assessing all aspects of your banking counterparty.
Create credit ratings in batch
Make a direct connection or upload a larger set of counterparties to generate ratings on the fly.
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✓ We connect to your system
Calibrated on financial databases
Calibrated across different global region, our model is fit for worldwide application.
Excellent model performance
Our model is calibrated on an annual basis to ensure optimal performance. Our model performance in best-in-class!
Financial information of up to five years is translated into ratios and trends, which form the basis of the quantitative score.
The quantitative score assesses the financial health on a number of pillars:
- Loan quality
The quantitative score is supplemented with additional insights like product and geographical diversification.
Adding the qualitative assessment leads to the ‘stand-alone score’.
Group support and environment
Taking into account support from related entities or governments, country (transfer) and industry risks leads to the final model score.
A comprehensive rating report is generated showing the final rating and the underlying components.
If necessary, an analyst can override the model score on the final rating.
Worried about onboarding, support or whether our
model fits your portfolio?
Customized models, because no portfolio is the same
More about model customization
Our credit risk experts can customize our model to fit your portfolio. Find out how we optimize our models for your use case
All support and updates are included
More about cost savings
Functional and technical questions are addressed quickly. Additionally, all our clients benefit from each upgrade to ensure continued compliance and ease of use.
Our quality guarantee
Data-driven calibration to optimize model performance
Annual recalibration of our model to incorporate latest economic trends. Using extensive databases containing thousands of banks
We include the insights from the portfolios of our clients, to improve our model performance