Digital credit scoring: How data analytics decide to whom to lend and not to lend money

When we open a bank account, apply for a loan or sign a mobile phone contract, we are usually asked to agree to a credit check – in Germany for example with Schufa. Those who request such a score about us hope to get a reliable statement about whether we are likely to meet our financial obligations. But how does this information come about? Big Data and Artificial Intelligence make it possible to use completely new strategies for this question, with which more individual, possibly more precise or even “fairer” scores could be created. This is where FinTechs come in, which – unlike Schufa – could basically use our entire digital footprint for their scores: Automated processes find patterns and correlations with which aspects of all areas of life can be translated into financial data. Whether the results, which include not only income and payment behaviour but also musical tastes and jogging routes, always correspond to reality is one question – another is whether personal characteristics play a role that are actually affected by the ban on discrimination. Because, as studies show time and again: AI models seem to systematically discriminate against socially disadvantaged groups. And loan sharks can also profit from this automated perpetuation of social injustice, depending on the legal situation.