## 1 Introduction  
Nowadays, most transactions take place online, meaning that credit cards and other payment systems are involved. These methods are convenient both for the companies and for the consumers. In the midst of all this are the banks, which must make sure that all the transactions are legal and non-fraudulent. This is an arduous and complicated task, due to fraudsters always trying to make every fraudulent transaction seem legitimate, which makes fraud detection a very challenging and difficult task [1]. For this reason, banks need to hire skilled software engineers and experts in fraud detection, but they also need to use specialized software, and altogether, it can be very expensive. Traditionally, fraud detection has been based on expert systems [2], which are techniques that solve problems and answers questions within a specific context. The great problem with these expert systems is that the more specialized they are, the more expensive they are to maintain [2]. Artificial intelligence (AI) has the potential to disrupt and redefine the existing financial services industry. In the general context of AI, there is machine learning (ML), which encompasses models for prediction and pattern recognition that require limited human intervention. In the financial services industry, the application of ML methods has the potential to improve outcomes for both businesses and consumers, and it can be a powerful tool against the credit fraud. At present, many works [1; 3; 4; 5] are being devoted to develop ML models against credit fraud. Using ML to generate prediction models can improve efficiency, reduce costs, enhance quality, and raise customer satisfaction [6]. Nevertheless, one of the big challenges and a potentially large obstacle in these models is their lack of transparency in decision making. These models are often black boxes, as we only know their inputs and outputs, but not the processes running inside. This makes them hard to comprehensively understand, and their properties are complicated to validate, so certain forms of risks could go undetected. This type of complexity constitutes a significant barrier to using ML in existing CFD [7; 8]. These black boxes have implications for financial supervisors, who will need to take account of the opportunities for enhanced compliance and safety created by ML, and to be aware of the ways that ML could be used to undermine the goals of existing regulations. For example, the United States prohibits discrimination based on several categories, including race, sex, and marital status. Moreover, a lending algorithm could be found in violation of this prohibition even if the algorithm does not directly use any of the prohibited categories, but rather uses data that may be highly correlated with protected categories. The lack of transparency could become an even more difficult problem in the European Union, where the General Data Protection Regulation adopted in 2016 and due to take effect in 2018 gives their citizens the right to receive explanations for decisions based solely on automated processing [9]. Even given this limitation, ML has potential applications in a variety of areas in financial services. The nature of opaque ML imposes significant limits on the use of ML for writing regulations [9]; for example, the U.S. prohibits discrimination on the basis of various categories including race, sex, and marital status [4]. Other of the consequences of black-box models are the potential biases in the results obtained and the difficulties involved in understanding the reasoning processes followed by the algorithms to reach specific conclusions [6; 10]. The data used to train the ML models may not be representative in fraud operations [9], thereby risking the recommendation of wrong decisions. Some firms emphasize the need for additional guidance on how to interpret current regulations. Towards breaking down this barrier, the interpretability of these models is fundamental.