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Risk Model Validation is a process used in data analytics to ensure that risk models are accurate and reliable. It involves testing the model's assumptions, data sources, and results to ensure that the model is fit for purpose. This process is used to identify any potential errors or biases in the model, and to ensure that the model is able to accurately predict risk. Risk Model Validation is an important part of the data analytics process, as it helps to ensure that the model is reliable and trustworthy.
Some companies in the Risk Model Validation market include Moody's Analytics, SAS, FICO, and IBM. Show Less Read more
