Manufacturers reduce stray losses in transformers by using magnetic shunts. These losses may cause hot spots in metallic structural parts of transformers such as the tank wall and frames. The calculation of the reduction of the stray losses on the tank wall using magnetic shunts is a complex task. In this study, a method for determining the position of a magnetic shunt and reducing the stray losses on the tank wall of a transformer is presented. Feed-forward neural networks (FFNNs) and the finite element method are employed. The Latin hypercube sampling is used to achieve a reduced number of training cases. Afterwards, several two-dimensional finite-element axisymmetric models of a transformer are solved and used as training data for two FFNN models. A comparison with finite-element models is carried out to evaluate the proposed method. Finally, a case study is solved using this method. The approach offers an alternative method to predict and reduce the intensity of the stray losses on the tank wall of the transformer in a straightforward way.