Classification Restricted Boltzmann Machine for comprehensible credit scoring model

Credit scoring is the assessment of the risk associated with a consumer (an organization or an individual) that apply for the credit. Therefore, the problem of credit scoring can be stated as a discrimination between those applicants whom the lender is confident will repay credit and those applicants who are considered by the lender as insufficiently reliable. In this work we propose a novel method for constructing comprehensible scoring model by applying Classification Restricted Boltzmann Machines (ClassRBM). In the first step we train the ClassRBM as a standalone classifier that has ability to predict credit status but does not contain interpretable structure. In order to obtain comprehensible model, first we evaluate the relevancy of each of binary features using ClassRBM and further we use these values to create the scoring table (scorecard). Additionally, we deal with the imbalanced data issue by proposing a procedure for determining the cutting point using the geometric mean of specificity and sensitivity. We evaluate our approach by comparing its performance with the results gained by other methods using four datasets from the credit scoring domain.