## 1 Introduction
### Background and Related Work  
Information technology plays an important role in combating money laundering and has attracted a lot of attention and concern from researchers and practitioners.  
Anti-money laundering solutions may involve applying different approaches such as focusing on suspicious transaction detection, pattern or anomaly detection, visual analysis or even security and control applications (see Section 3.3.1).  
Historically many support mechanisms have been proposed, developed and studied, including in the fight against other financial crimes such as fraud detection, as pointed out in Reference [6], which conducted a systematic review of the literature on the application of data mining techniques in detecting financial fraud. In this paper the authors analyzed 49 articles published between 1997 and 2008 classifying them into four categories of financial fraud (bank fraud, insurance fraud, securities and commodities fraud and other related financial fraud) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection and visualization).  
Among the selected publications, the work showed that the main data mining techniques used to detect financial fraud are logistic models, neural networks, the Bayesian belief network and decision trees. All of which provide primary solutions to the problems inherent in detection and classification of fraudulent data.  
Also focused on detecting financial fraud, [PERSON] [7] conducted a survey that categorized, compared and summarized several published technical and review articles on automated fraud detection by applying data mining techniques. The article also formalizes the main types and subtypes of known frauds and presents the nature of evidence from data collected in the affected industries.  
Application of statistical methods to combat financial crime was discussed by [PERSON] [8] in their work which aimed to conduct a survey of broad classes of methodologies accompanied by selected illustrative examples, while [PERSON] [9] also conducted a survey of common design approaches in the development of anti-money laundering software by conducting a detailed analysis of anomaly detection, machine learning and neural network techniques.  
A survey of the machine learning algorithms and methods applied to detect suspicious transactions focusing on machine learning was conducted by [PERSON] [10], where solutions like anti-money laundering typologies, link analysis, behavior modeling, risk score, anomaly detection and geographical capability were identified and analyzed.  
So, over the years, a number of reviews and surveys articles focused on solutions for the combat of financial crimes have appeared in conference or journal publications. However, there were no records of works aimed at systematic reviewing the literature on the application of technical solutions in approaches focused on the fight against money laundering.  
Through this systematic literature review, we are interested in finding out what approaches and support mechanisms are available and how they can contribute to the combat of money laundering. The paper is structured as follows--Section 2 describes the systematic literature review method used in this paper and defines the review protocol. Section 3 presents demographic information, quality assessment of included studies and research questions by analyzing selected studies with further discussion of results. Threats to the validity, implications and limitations of research are discussed in Section 4 and, finally, conclusions are presented in Section 5.