Customs duties are based on the origin and value of the goods and their classification (the customs tariff to be applied). Falsifying any of these factors when importing or exporting products is fraud. This includes falsely declaring the origin of the goods, declaring a lower value on the goods, misclassifying the goods and smuggling goods. In this paper we apply machine learning algorithms (Artificial Neural Network, Fuzzy Min-Max Classifier and Logistic Regression) for fraud detection in customs declarations. Performance of the models are evaluated using accuracy, sensitivity and specificity. The best results were achieved using Logistic Regression. In further research it would be useful to analyze applicability of ensemble learning methods and others fraud detection models.

Insurance fraud is one of the most expensive economic financial crimes. Most risk management solutions use rules to detect potential abuse, but as the patterns of abuse change, those solutions become ineffective. In this paper we apply machine learning (Decision Trees, Bagging, Random Forests and Boosting) for fraud detection in health insurance. Performance of the model is evaluated using accuracy, error rate, sensitivity and specificity. The best results were achieved using Bagging technique. In further research it would be useful to analyze applicability of deep learning models and anomaly detection methods.

Artificial intelligence uses in financial markets or business units forms financial innovations. These innovations are the key indicator for economic grow and intelligent finance system formation. Recants years scientist and most innovation driving companies, such as Google, IBM, Microsoft and other, are focusing in deep learning methods. These methods have achieved significant performances in diverse areas: image recognition, natural language processing, speech recognition, video processing, etc. Therefore, it is necessary to understand the variety of deep learning methods and only then their applicability in the financial field. Accordingly, in this paper firstly is presented differences in science community already settled deep learning method’s architectures. Secondly, is shown a big picture of developing scientific articles of deep learning uses in finance field, where the most used deep learning methods were identified. Finally, the conclusion, limitations and future work have been shown.