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Case Study

Risk Modelling for Credit Insurance

FOCUS ON: Risk, Portfolio risk management, Avantage,

The insurance company is the world’s leading provider of trade-related insurance solutions for corporations of all sizes. Its knowledge helps companies choose the right customers to do business with, while offering the reassurance that invoices will always be paid. Its solutions consist of: credit insurance, debt collection, surety bonds and fraud insurance. Avantage Reply supported the insurance company’s Risk Modelling team.

Solvency II and Credit Risk

The client has developed and put into practice an internal model to calculate the Solvency II Capital Requirements. The client’s risk modelling team were looking for expert support for the monthly and quarterly processes regarding the quantification of the credit risk linked to their insurance portfolio. In particular, assistance was required for the quarterly process held to define and validate the parameters and Expected Loss used as inputs for the model calculations. The parameters are calculated by the Risk Management team, calibrated and validated at local level and finally consolidated at the Group level.

The Avantage Reply Approach

Avantage Reply supports the local team in producing the statistics for the quarterly review of the model’s parameters of all modelled Group branches and, in executing the quarterly runs of the model to compute the Solvency Capital Requirement (SCR).

With the support of Avantage Reply, a new Data-Quality framework has been developed and the existing multiple programs developed using SAS and VBA codes have been unified under a common SAS project where SAS codes have been reviewed and simplified in order to provide a faster, more efficient and automated approach in the computation of the SCR and in the production of the different output reportings.

This approach allowed the insurance company to better control their data quality process which effectively minimized their data issues. Through the automation of the process, the preparation of the quarterly model runs can now be executed much more easily and efficiently.

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