## 3 Materials and Methods
### Informative Variable Identifier
## 4 Experiments and Results

In this work, we propose a novel methodology to simultaneously face the double challenge of applying new, powerful, and proven AI tools, while maintaining the interpretability of the underlying descriptors, thereby allowing compliance with the rigorous regulations of data protection and non-discrimination in force for financial institutions. The developed methodology helps the interpretable linear methods by capturing the relevant features and leaving aside the black boxes, while minimizing the potential bias. To do so, experiments for both synthetic and real data were performed as follows. First, we compared a number of the ML methods with the IVI technique in order to evaluate their capacities for automatic classification. Second, we modeled different learning architectures that allowed us to evaluate the predictive value of the result (CFD), using various sets and subsets of features. This second analysis offers a quantified vision of the predictive capacities of the method-features pairs, to adequately qualify the different options. Third, a detailed evaluation of the incremental predicted value was performed for each of the previously defined methods. We analyzed them feature by feature, along with the speed of convergence and the predictive capacity of each. A final exercise in the analysis of the coefficients, applied to each of the features in the different methods, allowed us to assess the contributions (interpretability) of the different features to the final predictions. This section is divided into two main subsections presenting the experiments on the synthetic and real datasets. Prior to the experiments, the datasets are introduced. The general strategy guiding the experimentation was to scrutinize and fine-tune the synthetic dataset to validate the methodology, for later evaluation of the generalization capabilities to actual CFD cases in the real dataset. ### Datasets

Although there are a large number of articles published about CFDs, it is not easy to access the actual data used due to data protection and confidentiality restrictions. That is why in this work, initial analysis has been prepared using surrogate signals generated by the authors, which helped us to define and model the detailed study to be carried out. This analysis process based on synthetic signals offered the required flexibility to evaluate the predictive capacity of the different variables, thanks to the effective knowledge provided by having built it. The knowledge acquired during this process allowed us to subsequently analyze the eventual generalization on the real dataset. For this last step, we used the database provided by Strathclyde University [35]. Synthetic Dataset. The first dataset introduces a synthetic linear classification problem with a binary output variable, and it was developed for the original proposal of the IVI algorithm [13]. It has 485 input features. In this work, and for reasons of representability and execution time, we have used a subset of features while keeping the feature names. The dataset used here included a set of 23 input features distributed as follows: 11 input features were drawn from a normal distribution, and 5 of them were used to linearly generate a binary output variable, specifically \(f0,f1,f2,f3\), and \(f4\). Therefore, these five features are informative for the problem. A set of another 12 features were randomly created with no relation to the previous ones, so that they could be considered as noisy and non-informative variables. Additionally, a new group of six variables were computed as redundant with the informative input features. German Credit Dataset. This repository is known as the German credit fraud (Statog) [35], and it contains real data used to evaluate credit applications in Germany. We used a version of this dataset that was produced by Strathclyde University. The German credit dataset contains information on 1000 loan applicants. Each applicant is described by a set of 20 different features.