Reliable forecasts based on our unique pool of data

The more data is available, the more reliable are the forecasts. As the leading credit bureau, we have a unique pool of data records on 67.9 million persons and 6 million companies.

The score values we calculate for consumers are based on the data SCHUFA has stored about your person, which you can view in your SCHUFA report. The information stored includes the number and kind of borrowing activities, any default of payment or information about your experience regarding borrowing.

Our methods: reviewed and found to be "very suitable"

Our score calculation methods have been developed by our experienced mathematicians and statisticians. The method used is called "logistic regression" which is a well-founded mathematical-statistical method tried and tested in practice for a long time for the prediction of risk occurrence probabilities.

Several impartial universities reviewed our method and found it to be "very well suitable to model the probabilities of an event occurring and to create score cards". The supervisory authority competent for SCHUFA has reviewed our scoring methods and found that they are reliable as regards data protection regulations.

We do not use geodata per default

Due to our special data pool of credit rating information, we do as a standard, i.e. in 99.7 percent of all score calculations, not perform a geoscoring. It does not matter if you are living in a "good" or "less good" region. Only in very few exceptional cases, i.e. if we do not have any information about the person inquired about, do we fall back on address data - and only if this is expressly requested by our customer. If SCHUFA does not know a person, the inquiring company frequently finds that the risk of lending is too high. In such a case, risk assessment based on geodata may result in the customer nevertheless being offered the loan it desires.

We also do without social scoring

We do not use any information from social media to compute our scores, likewise no names or other discriminating data.