Glossary of terms

• Instant payment: Transfers that make the amount being transferred available to the payee within ten seconds of receipt of the order. In the EU, this has been possible since 2017 using the SEPA Instant Credit Transfer scheme.
• Machine Learning (ML): A sub-field of Artificial Intelligence. It is based on applied statistics and mathematical optimisation. There are various definitions of ML. In its study “Big Data meets Artificial Intelligence”, BaFin defines Machine Learning very broadly as the notion of giving computers the ability to learn from data and experience through suitable algorithms. Compared with rule-based approaches, the system learns without the programmer specifying which outcomes should be derived from certain data constellations, and how. Computers can thus construct a model of their world and better solve the tasks that are assigned to them.
• Linear/logistic regression: A classic statistical methodology that has proven its worth in practice for many decades and is now used in ML. The coefficients (influencing variables) of the models generated by the regression can be easily analysed and interpreted. This makes the model transparent and a powerful tool for data analysis. However, regression is limited to linear models and therefore cannot normally adequately capture more complex correlations.²
• MaRisk: Based on section 25a of the German Banking Act (Kreditwesengesetz – KWG), the Minimum Requirements for Risk Management (MaRisk) provide banks with a comprehensive, principle-based framework developed together with the banking industry, which at the same time still gives institutions scope for tailored implementation.
• Schufa score: Schufa Holding AG is a credit agency in which savings banks and cooperative banks hold a majority interest. The Schufa score provides information on the probability that an individual or company will meet their payment obligations. To do this, Schufa evaluates large quantities of data, some of which it receives directly from partner banks. Various characteristics play a role here, such as whether the client has repaid previous loans or how many accounts, loans and credit cards they have. The Schufa score is used by very many banks in Germany when lending money, although mobile phone companies also use it. Consumers can request their personal Schufa score once a year free of charge (copy of the personal data under Article 15 of the GDPR) and review it for any errors in the data.