The quantification of model risk is a term that mainly appears in the fields of Artificial Intelligence, Big Data and Smart Data, as well as crowdfunding and finance. It describes the measurement and assessment of uncertainties that arise when companies use computer or computational models for important decisions. Model risks arise because models can never fully represent reality \u2013 flawed assumptions, poor data quality, or unexpected events can lead to incorrect results.<\/p>\n
For example: A bank uses software to decide whether someone is granted a loan. If the model makes errors, it can happen that solvent customers are rejected or risky loans are issued. By quantifying model risks, the bank can assess the extent of this uncertainty and how much it affects the business. Accordingly, it can take measures to reduce the risk.<\/p>\n
Quantifying model risks therefore helps companies to better assess the reliability of their digital models. This enables them to make decisions more responsibly and avoid unwanted surprises.<\/p>","protected":false},"excerpt":{"rendered":"