## 1 Introduction
The justification for this type of regulation lies in the potential bias that the hidden stages of the model could be applying, thus leaving the individual, the regulatory body, and the risk assessment entity devoid of tools to identify any undesirable situations that finally might be reproducing [6; 10]. Even more, the data used to train the ML models may not be representative for the problem [8], sometimes driving eventually to inaccurate models, with limited generalization capabilities. Having said that, and returning to regulatory restrictions, the entities understand the need for a regulation that ensures thatthe use of technology cannot inadvertently cause discriminatory treatment of people, but they also agree on the need for a clearer guidance from the authorities that offer a reasonable path towards the necessary and effective application of AI in this field [11]. Considering that regulatory bodies and administrative authorities will not allow financial institutions to adopt AI models without addressing the necessary description of the decision process being followed [7], a suitable way to overcome the regulatory issues and the mistrust with respect to the algorithms being used is to provide the regulators, authorities, and financial entities with supplemental environments and tools that contribute in an effectual way to the real interpretability. Therefore, we can state that decision models should be easy to understand, meaningful, and traceable. This last one means that each initial variable or feature needs to be linked to final decision score through a visible value, process, or function [7,9,12]. To accomplish this challenging goal, state-of-the-art methods in novelty detection such as autoencoders can be extremely useful, as well as a new set of strategies to offer interpretability on what was traditionally considered a black-box model. Under this perspective, the contribution of this work is a novel methodology to address the mentioned complexity. The methodology proposed in this work has a triple objective: First, to reduce the dimensionality by selecting the informative features; second, to efficiently compress and encode data to isolate fraud transactions from non-fraudulent ones; third, to propose, and eventually evaluate, novel techniques to offer a comprehensive explanatory model in CFD. To achieve this, we propose an explanation at the level of a single instance artificially generating a set of data around said instance (through random sampling and using controlled perturbations), and finally, applying a linear learning model to the distance between the instance and the sampling data. This last step represents the main difference with respect to previous applications in terms of the ability to tie input features to the outputs, thus providing the desirable interpretability. To approach the dimensionality reduction, we use the positive results included in the companion paper [1] where we applied a novel feature selection technique, the informative variable identifier (IVI) [13], which can distinguish among informative, redundant, and noisy variables or features. 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, a summary of new nonlinear ML algorithms used in this work is described, as well as explanatory strategies to convey an effective interpretation, as single transaction-level explanation (STE) and individual transaction rankings (ITR) are introduced and formally described. In Section 4, the different datasets are defined, and we present the qualitative and quantitative benchmarking over different datasets while maintaining the interpretability. Finally, in Section 5, discussion and observations are given, and conclusions are summarized.