Deep Learning is a consolidated, state-of-the-art Machine Learning tool to\nfit a function when provided with large data sets of examples. However, in\nregression tasks, the straightforward application of Deep Learning models\nprovides a point estimate of the target. In addition, the model does not take\ninto account the uncertainty of a prediction. This represents a great\nlimitation for tasks where communicating an erroneous prediction carries a\nrisk. In this paper we tackle a real-world problem of forecasting impending\nfinancial expenses and incomings of customers, while displaying predictable\nmonetary amounts on a mobile app. In this context, we investigate if we would\nobtain an advantage by applying Deep Learning models with a Heteroscedastic\nmodel of the variance of a network's output. Experimentally, we achieve a\nhigher accuracy than non-trivial baselines. More importantly, we introduce a\nmechanism to discard low-confidence predictions, which means that they will not\nbe visible to users. This should help enhance the user experience of our\nproduct.\n