Risk Solution Network

Risk Solution Network AG is a subsidiary of the Swiss cantonal banks of Basel, Lucerne and St. Gallen. Aiming to be the leading outsourcing structure in credit-risk management for small and medium-sized Swiss financial institutions, it provides more than 30 banks with a set of tools designed to measure and validate hedge credit risks: financial analysis, rating, loss given default and pricing concepts. Those tools constitute a common pool of data, which is the basis for enhancing and validating the models and for tailored services such as reporting and benchmarking. RSN also offers its customers a platform on which to exchange knowledge and experience and to represent their interests together towards external parties.

From financial analyses, default data and other qualitative criteria, RSN identified a set of distinguishing key indicators and qualitative factors. It was not only looking for indicators of short-term risk but for factors that lead to positive constant company development. Weighting all of those factors correctly is what an efficient rating model needs, both as a basis for credit approvals and for fair risk pricing, and that requires highly developed and stable optimisation algorithms.

Prospero improved RSN's rating processes with the DetectX® Credit Risk Rating solution. The challenge was to find scoring processes that would improve the Gini coefficients or the ROC curve while abiding by several restrictions predetermined by RSN. The optimised scoring calculations constitute the basis of the rating models used at RSN banks to rate customers in the small and medium enterprise segment.

In the second phase there was a three-day training for RSN employees. They use DetectX® Credit Risk Rating for the creation, validation and calibration of their rating models.

With the DetectX® Credit Risk Rating solution, Basel III requirements can be implemented in a secure, consistent and law-abiding way. Users benefit from the system's learning process, which comes from creating forward-looking rating models and from validating or calibrating existing ones: the system itself finds error-minimised rating models with maximised separation power. A user can simulate different risk strategies and optimise the credit business. The models created are based on the fewest possible attributes, so the reasoning behind a rating stays easy to explain, and all activities are tracked and may be recorded at any time.