  Deep Learning is a consolidated, state-of-the-art Machine Learning tool to
fit a function when provided with large data sets of examples. However, in
regression tasks, the straightforward application of Deep Learning models
provides a point estimate of the target. In addition, the model does not take
into account the uncertainty of a prediction. This represents a great
limitation for tasks where communicating an erroneous prediction carries a
risk. In this paper we tackle a real-world problem of forecasting impending
financial expenses and incomings of customers, while displaying predictable
monetary amounts on a mobile app. In this context, we investigate if we would
obtain an advantage by applying Deep Learning models with a Heteroscedastic
model of the variance of a network's output. Experimentally, we achieve a
higher accuracy than non-trivial baselines. More importantly, we introduce a
mechanism to discard low-confidence predictions, which means that they will not
be visible to users. This should help enhance the user experience of our
product.
