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.<\/p>\n
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 \u2013 for example, by regularly monitoring the results or establishing clear rules for when the model is used.<\/p>\n
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.<\/p>","protected":false},"excerpt":{"rendered":"