## 1 Introduction
The regulation authorities are composed by humans, and in this sense the explanations must be understood by humans. In like fashion, the decision models need to be an easily understandable, or in other words, they need to allow us to check which attributes are necessary to produce explanations that are comprehensible [11]. On the one hand, the easiest way to achieve interpretability is to use globally interpretable models, meaning that they have meaningful parameters (and features) from which useful information can be extracted in order to explain predictions [11], such as linear regression or other linear models, including gradient boosting, support vector machines, and linear discriminant analysis. On the other hand, to achieve high accuracy, it is necessary to select the most representative variables for linear models. Hence, the methodology proposed in this present work has as two objectives: first, to reduce the dimensionality while selecting the informative features; and second, to use interpretable models to produce explanations comprehensible to humans. In the companion paper [12], and after different strategies to obtain interpretability in linear models have been developed and compared herein, we present a thoughtful analysis of non-linear models. We aimed to create a reliable, unbiased, and interpretable methodology to automatically measure credit fraud detection (CFD) risk. To do so, we emulated a controlled environment using a synthetic dataset that allowed us to propose detailed analysis of all possible variables. The results obtained in this closed and controlled environment, together with the knowledge acquired, allowed us to perform validation on a real database. The proposed methodology incorporates a recently published novel feature selection technique, called informative variable identifier (IVI) [13], which is capable of distinguishing among informative and noisy variables. In the original work, IVI was implemented using only one ML method. We further extended this method and performed intensive benchmarking of a set of different techniques, to extend the method's validation and to enhance its generalization capabilities in the context of CFD applications. Different subsets of relevant features were obtained as a result of this exploration. We classified them according to newly proposed innovative filters, and to attend to recurrence, according to recurrent feature filter (RFF) and maximally-informative feature filter (MIFF). This reclassification allowed dimensionality reduction: an improvement in accuracy can be obtained as a consequence. In the method, interpretability and traceability are maintained over the process by using linear methods in the matching of each transaction and its corresponding evaluation. Therefore, the contribution of each informative feature obtained in this final model forms a straightforward indication for further legal auditing and regulatory compliance. This work is organized as follows. A short review of the vast literature in the field of CFD and ML-based systems is presented in Section 2. In Section 3, the IVI algorithm and the RFF and MIFF filters are described in detail. In Section 4, first, the synthetic and the German credit datasets we used are described. Then, we present the qualitative and quantitative benchmarking on synthetic data and a different analysis on a German credit dataset. Finally, in Section 5, discussion and observations are presented and conclusions are stated.