An Entropy Based Algorithm for Credit Scoring

Abstract

Part 6: Decision Support in EISInternational audienceThe request of effective credit scoring models is rising in these last decades, due to the increase of consumer lending. Their objective is to divide the loan applicants into two classes, reliable or unreliable, on the basis of the available information. The linear discriminant analysis is one of the most common techniques used to define these models, although this simple parametric statistical method does not overcome some problems, the most important of which is the imbalanced distribution of data by classes. It happens since the number of default cases is much smaller than that of non-default ones, a scenario that reduces the effectiveness of the machine learning approaches, e.g., neural networks and random forests. The in Maximum Entropy (DME) approach proposed in this paper leads toward two interesting results: on the one hand, it evaluates the new loan applications in terms of maximum entropy difference between their features and those of the non-default past cases, using for the model training only these last cases, overcoming the imbalanced learning issue; on the other hand, it operates proactively, overcoming the cold-start problem. Our model has been evaluated by using two real-world datasets with an imbalanced distribution of data, comparing its performance to that of the most performing state-of-the-art approach: random forests

Credit scoring by leveraging an ensemble stochastic criterion in a transformed feature space

“…The first branch of techniques treats the credit scoring problem as a data analysis or data transformation problem, discriminating reliable and unreliable samples by investigating data disturbance or analyzing new transformed spaces. The work of Carta et al [13,54] followed that direction by investigating data entropy before and after an unknown sample is inserted in a dataset in order to measure how it is affected, with this information being helpful to detect default (or unreliable) cases. Fan et al [30] also exploited the entropy criterion in order to face the issues related to imbalanced datasets.…”