## 5 Discussion and Conclusions  
In this article, we elaborated on the possibility of applying, today, ubiquitous ML techniques to CFDs and providing interpretability to those decisions made in ML models. We have extended here the analysis to nonlinear models with respect to the companion work [1]. One of the main drawbacks of these technologies is that, even though extremely effective and powerful in all disciplines where they were applied, they are mostly presented to users as black boxes where it is virtually impossible to decode the way the features are treated internally. This last statement is intrinsically incompatible with regulation issued by administrative bodies, as whatever tool used should be compliant with non-discriminatory rules and transparency. In an attempt to deal with such a difficult dichotomy, in the companion paper [1], we evaluated different techniques to identify in an effective way the informative features and their relationships and to minimize potential biases. In this work, we proposed to evaluate and present a methodology to obtain interpretability in nonlinear models, and, in particular, we worked with autoencoders. To achieve this, through STE we are able to effectively identify the main features in the decision process, thus providing interpretability, and hence leaving aside black boxes through the use of state-of-the-art technology in ML techniques. We claim that it is possible to build robust explanatory models to simultaneously meet the regulatory constraint while using the power of the ML techniques. To achieve this, we first developed the synthetic dataset to define and fine-tune the models, and successful models were later applied to two real datasets to verify their generalization and consistency. The main conclusions when analyzing the three datasets are summarized next. * We have verified the results obtained in the companion paper [1], that is, using the IVI algorithm with MIF filter in a new real dataset, we can systematically capture all the real features with informative values. * The better results obtained with the proposed approach (accuracy increase of 5%) suggested that the use of the presented method can improve the performance, meanwhile the reduction in terms of features simultaneously can enhance the computer efficiency. * The use of STE has proven to be a suitable method to interpret the relationship between the contribution of each feature and the output of the classifier in black box methods. * The use of ITR methods is proposed as a novel technique to classify transactions that are similar in terms of the participation of the variables in the classifier result. \begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & \(\mathbf{\tau}\) \\ \hline Synthetic & 0.803 \\ PaySim & 0.900 \\ German & 0.696 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean of all Kendall correlation coefficients for each dataset. This value represents, in absolute value, the inverse necessary complexity to approximate the underlying reality with a linear model. Figure 10: Distribution. Kendall rank correlation coefficient for each dataset: (a) Synthetic dataset; (b) PaySim Dataset; (c) German Dataset. The results of applying these findings over the German Credit Dataset [47; 48] were confirmed consistent with previous results in our synthetic dataset, as well as with other public published studies. It is also interesting to note that features picked by the model were consistent with those ones from published works sourcing the very same datasets [49; 50]. Key features found in our case were livingbeyond means, lack or absence of transaction trail, unexpected overdrafts or declines in cash balances, and carloans. It is interesting to note that in the new dataset one of the features marked as relevant was isFlaggedFraud, which is a flag decided by a fraud analyst expert and it has high accuracy rate by itself. It was clear from the experiments that in the latent space the ML algorithms improve the classification task by better mapping the different types of transactions.