What is the SCHUFA score?

The SCHUFA score is usually understood as the basic score explained below. This is intended to predict the probability that a private individual will meet the payment of a loan or other non-recurring payment on time.

This score is provided by SCHUFA Holding AG, the largest German credit agency with its headquarters in Wiesbaden. SCHUFA receives the data for calculating this score from banks, telecommunications providers and other companies, which in turn use the calculated SCHUFA values.

In fact, a private individual does not only have the basic score, but also several industry scores.

The goal of scoring

The idea of scoring is to use data from the past to forecast the future as accurately as possible.
This is intended both to give companies security and to help borrowers as a whole.
On the one hand, payment defaults should be kept to a minimum and, on the other hand, excessive loans that could result from “black sheep” should be prevented.

basic score

The base score is a value of 0-100 that indicates how likely it is that a person will meet a financial obligation, although a high value is better. The average value in Germany was 97.8% in 2016.
The basic score is updated every 3 months.

industry scores

Industry scores should represent the creditworthiness of a person in relation to the respective business segment.
At the moment there are 7 different scores for each person:

[list] [*]Schufa score for banks
[*]Schufa score for the mortgage business
[*]Schufa score for trade
[*]Schufa score for mail order & eCommerce
[*]Schufa score for telecommunications companies
[*]Schufa score for freelancers
[*]Schufa score for small traders
[/list]

Here, in addition to the score value (this value has been between 0 and 9999 since 2008), there is a “ranking level” (A-F, where A is the best) and a “fulfillment probability”, similar to the basic score, of 0%-!00%.
In contrast to the basic score, industry scores are updated daily. An insight into these scores must be requested separately.

Incoming data and score optimization

While personal data such as name or date of birth are also stored, the following data in particular flow into the score:

[list] [*]Previous payment defaults
[*]Use of credit
[*]Credit activity
[*]Longer credit history
[*]General data
[*]Address data
[/list]

According to SCHUFA, geoscoring data are only used in rare cases, i.e. the score of a person is calculated from the data of persons living in the same area. This option is only used if there is no other information about a person that could make it possible to calculate a score.
Data from the social media are not used by SCHUFA.

In order to improve its score, in addition to paying bills on time, “simplicity” is mainly recommended. This means that more frequent changes of flats and accounts, but also the possession of many credit cards or similar have a negative effect on the personal score.
It should be emphasised that a credit paid on time is better for the score than never having taken out a loan.

transparency

The most frequent accusation against SCHUFA is a criticism of a lack of transparency. In the course of demands for the disclosure of her formulas for the scorecalculation, she invoked the trade secret. The most recent award made to SCHUFA on this point was on 28 January 2014 by the Federal Supreme Court (BGH).
The main argument here is that with accurate disclosure. The Commission considers that the value of the formulas could be used by consumers to manipulate their scores, which would render the value meaningless.
It is only known that a logistic regression model (logit model) is used to determine the score.