An increasing number of FinTech companies in the EU and the US offer creditworthiness analyses based upon Big Data. Big Data describes massive amounts of structured and unstructured data (measured in Tera- and Petabytes) drawn from a wide variety of sources including social network profiles, web surfing behavior, SMS, Blogs, and Tweets. These analyses allow deep insights into a consumer’s psychology due to the prediction of that individual’s creditworthiness, willingness-to-pay, or switching behavior. The providers of these insights use methods such as machine learning and data mining, which reach far beyond traditional scoring models in terms of complexity. Thus, they pose great challenges for Data Protection Authorities as well as for Financial Supervisors.
In a short piece recently published by Wirtschaftsdienst BCCP Senior Fellow Nicola Jentzsch discusses these recent changes, their impact on competition at different levels of the value chain in the consumer credit market, and the challenges they pose for supervisors. The latter are primarily related to understanding the risk implications of these models in banking, the integrity of the data used, as well as potentially emerging unlawful discrimination tendencies.
Moreover, at this stage, it is rather unclear how basic data protection rights, such as the right to access and notification, rectification and information on the logic of an applied automated decision can be transferred to situations where Big Data analyses are used. It is very clear, however, that these questions can only be answered if supervisors are far better equipped in future to deal with such technological developments.
Read the full article at Wirtschaftsdienst (in German).