The module will start by defining the concept of Knowledge Discovery in Data (KDD) as consisting of three steps: data pre-processing, data mining and post-processing. Next, we will zoom into the data mining step and distinguish two types of data mining: descriptive data mining (e.g. clustering, association and sequence rules) and predictive data mining (e.g. regression and classification). The module will then illustrate how KDD can be successfully used to develop credit scoring applications, where the aim is to distinguish good customers from bad customers (defaulters) given their characteristics. The importance of developing good credit scoring models will be highlighted in the context of the Basel II and III guidelines. The theoretical concepts will be illustrated using real-life credit scoring cases and the SAS Enterprise Miner software.