## 5 Discussion and Conclusions
The use of latent spaces could still be considered as a black-box model. With the aim of mitigating explanations for black-box, we have introduced two new mechanisms. First, STE summarizes the contribution of each feature for an individual transaction based on small-scale fluctuations, and second, the ITR method is able to build an individual feature ranking for each transaction. These rankings represent a closer estimation of those features that are more important than the others in the decision process for an individual transaction. The rationale of the ITR-based approach is a single-instance-level explanation for each transaction, which allows us to detect similar transaction profiles for the transaction with equivalent ITR. With these profiles, we can detect possible transaction biases caused by giving too much importance to not-allowed features, and then producing discrimination based on various categories including, for instance, race, sex, or marital status. We also may disclose the strong relation between STE and ITR. In the experiment where we verify how small variation in a feature in input space has different response in the latent space, we discovered that the feature newbalanceOrig has a high impact on this small variation, and this was confirmed when we generated the different profiles with ITR. In addition to what is expressed in these conclusions regarding the potentiality in terms of the explicability shown, the evaluation of the Kendall correlation of ITR throughout the different datasets showed interesting results that encourage the deepening of the proposed analysis. In this sense, the differences in the means and distributions of the Kendall correlation, for the different datasets, can be interpreted in several directions. On the one hand is the existence of modalities in the distributions, which correspond to the existence of a number of different models needed to approximate the underlying reality that may be related to the number of different sets of transactions that take place. This set of transactions should not necessarily coincide with the classes under study, but with different realities, or varieties, which should be studied individually and separately for a better understanding of the sample base for greater interpretability. On the other hand, the presence of a single modality would indicate that of a linear, unique, and representative model, capable of evaluating with at least the same precision as the highly complex model evaluated. Thirdly, the existence of a non-modal distribution, whether uniform, Gaussian, or of any other type, could suggest various interpretations that in all cases could suggest facing new methods of analysis, either due to the existence of infinite linear models, equivalents, or a limited number of nonlinear models. In this direction, it is necessary to point out that although it is possible for each and every one of the transactions to obtain an ITR model, which provides interpretability to the proposed classification, it will be offered solely and exclusively for that transaction, not being possible to generalize to other cases. This local approximation and STE approach, could be understood as an advantage when it comes to interpretability, although its unique single explanation could also make regulators and authorities reluctant to validate extensively. That is why it is proposed, as the next step of this work, to advance in the knowledge of these distributions and the data models that give rise to them in order to also be able to propose interpretable and generalizable nonlinear models that ensure consistency, if not for the total of samples of the set, at least for a large group of them that are part of subsets that share the same ITR. We can conclude that our methodology provides a detailed evaluation at the transaction level, adding interpretability to each transaction and making visible the most relevant features in decision process.