Artificial intelligence can offer significant benefits that support our mission, but the use of such technology may also increase the risk of discrimination and abuse of authority. It may also increase risks to our customers’ privacy.
Lånekassen does not use AI to make decisions on applications. However, we may use AI as a tool to forecast Lånekassen’s case volume, to evaluate our various support schemes, and to develop IT systems. Lånekassen may also use AI as a tool to provide tailored information to our customers.
Use of machine learning in information initiatives aimed at students who state that they live away from their parents
Lånekassen will use AI in the form of machine learning to provide tailored information to students who have stated that they live away from their parents. A selection of these students will receive reminders emphasising the importance of providing accurate information about their place of residence. The purpose of this information initiative is to help ensure that support from Lånekassen goes to those who are entitled to it, and no one else.
To guide our customers as effectively as possible, we will use a machine-learning model that suggests who should receive this information. Receiving the information does not mean that Lånekassen believes the customer has provided incorrect details. However, a machine-learning model can help ensure that the information reaches the most relevant recipients more accurately than broad general communication to large customer groups without the use of machine learning.
Developing the model involves assessing which types of information enable the model to make accurate suggestions. The model is trained using results from previous checks of students who have stated that they live away from their parents (residence verifications), as well as personal data collected when we process applications for student financial support.
The personal data used include, for example, completed study credits, age, place of study and place of residence. The model analyses which factors increase the likelihood that customers are not registered with the correct residential address in Lånekassen’s records, and which factors decrease that likelihood.
Experience from previous residence verifications shows that some of the most important factors identified by the model include the distance between the parents’ home and the place of study, the student’s stated place of residence, and how far the student has progressed in their studies.
Our use of this model is considered profiling pursuant to the General Data Protection Regulation. You can read more about your rights in our privacy statement.