Identifying risky bank loans using Decision Trees In this section, we will develop a simple credit approval model using C5.0 decision trees. We will also see how the results of the model can be tuned to minimize errors that result in a financial loss for the institution. Step 1 – Collecting Data The idea behind our credit model is to identify factors that are predictive of higher risk of default. Therefore, we need to obtain data on a large number of past bank loans and whether the loan went into default, as well as information on the applicant. Data with these characteristics is available in a dataset donated to the UCI Machine Learning Data Repository (http://archive.ics.uci.edu/ml) by Hans Hofmann of the University of Hamburg. The dataset contains information on loans obtained from a credit agency in Germany. The credit dataset includes 1,000 examples on loans, plus a set of numeric and nominal features indicating the characteristics of the loan and the loan applicant. A