Interpretability methods applied to fraud detection intelligent systems: a systematic review

In a technological world, in which data is generated exponentially, financial analysis has gradually become more important to avoid large losses due to fraud. Considering the large volume and the difficulty of human data checking, machine learning technologies have become one of the main tools to solve the problem. However, due to the creation of data protection laws in several countries, in some scenarios the detection of fraud through intelligence algorithms becomes insufficient. Therefore, it is necessary to understand how the algorithm actually labels a transaction as fraudulent or not. In this work, presented as a systematic literature review, we look for answers on how explicable/interpretable fraud detection algorithms have been applied in order to solve the problem of illegal activities in the financial sector. As a result of the mapping of the current state of the art, this work highlights the gaps in the literature and present the scenario of interpretable techniques used for fraud detection comprehension.