Risk-oriented model management is particularly important in the areas of Big Data and Smart Data, Artificial Intelligence, and Digital Transformation. Companies today use numerous data-based models, for example, to create forecasts or make decisions. Risk-oriented model management helps to identify and minimise potential risks early on during the development, use, and monitoring of such models.
A practical example: An online shop uses an AI model to make purchase recommendations for customers. If this model is incorrect, it can lead to a loss of sales or the dissemination of false information. Risk-oriented model management therefore checks where the model could make mistakes and what the consequences would be. Targeted measures are then taken to reduce these risks – for example, by regularly monitoring the results or establishing clear rules for when the model is used.
This is how risk-oriented model management ensures that digital solutions remain secure and trustworthy. It helps companies keep track and prevent damage from faulty models. This approach is essential, especially in a world that is becoming increasingly data-driven.
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