A computer-implemented method for detecting anomalies in data communications comprising: - obtaining a set of data points from a relational database wherein each data point is defined by a datum that is a continuous numeric variable and at least one category of the datum that is a discrete numeric variable; - preprocessing the obtained set of data points to introduce the set of data points into a non-parametric statistical model , standardizing the data and extracting at least one combination of categories from the datum; - performing a grid search to obtain at least one combination of hyperparameters with which to train the non-parametric model ; - applying a radial basis kernel function, RBF, to identify anomalies in a given application time period using a non-linear decision threshold and upper and lower limits obtained by means of a statistical model to detect anomalies in a historical time period containing the given application time period, wherein the statistical model is selected between the non-parametric model and a parametric model , and wherein the statistical model persists the upper and lower limits obtained in the relational database; - if the selected statistical model is the non-parametric model , the used decision threshold is calculated according to the obtained hyperparameters and for each extracted combination of categories of the data, and the non-parametric model calculates a distance from the continuous variable of the standardized data to the decision threshold value calculated for said variable and for each combination of values of the discrete variables of data categories; - displaying in a graphical user interface results obtained from comparing the upper and lower limits obtained with statistical information associated with the set of data points, wherein the statistical information, if the selected statistical model is the non-parametric model , is the distance calculated for each datum and combination of data categories. - obtaining a set of data points from a relational database wherein each data point is defined by a datum that is a continuous numeric variable and at least one category of the datum that is a discrete numeric variable; - preprocessing the obtained set of data points to introduce the set of data points into a non-parametric statistical model , standardizing the data and extracting at least one combination of categories from the datum; - performing a grid search to obtain at least one combination of hyperparameters with which to train the non-parametric model ; - applying a radial basis kernel function, RBF, to identify anomalies in a given application time period using a non-linear decision threshold and upper and lower limits obtained by means of a statistical model to detect anomalies in a historical time period containing the given application time period, wherein the statistical model is selected between the non-parametric model and a parametric model , and wherein the statistical model persists the upper and lower limits obtained in the relational database; - if the selected statistical model is the non-parametric model , the used decision threshold is calculated according to the obtained hyperparameters and for each extracted combination of categories of the data, and the non-parametric model calculates a distance from the continuous variable of the standardized data to the decision threshold value calculated for said variable and for each combination of values of the discrete variables of data categories; - displaying in a graphical user interface results obtained from comparing the upper and lower limits obtained with statistical information associated with the set of data points, wherein the statistical information, if the selected statistical model is the non-parametric model , is the distance calculated for each datum and combination of data categories. <EOS>