Many machine learning algorithms for tabular data produce black-box models, which prevent users from understanding the rationale behind the model predictions. To overcome these limitations, an approach is proposed, called ViaSHAP, which employs both universal approximation theorem and olmogorov-Arnold representation theorem to learn functions that can compute exact feature attributions using the Shapley value and also provide high predective performance. A large-scale empirical investigation is presented, showing that the feature attributions provided by ViaSHAP align with Shapley values that are computed using a post-hoc algorithm. Furthermore, the initial results show that ViaSHAP outperforms three powerful machine learning algorithms for tabular data, Random Forests, TabNet, and XGBoost.