## 3 Results and Discussions
### Question Analysis
#### 3.3.1 Approaches and Application Domains (RQ1 e RQ2)  
For each selected work, the adopted approaches were identified, descriptions are presented in Table 10 and their respective purposes/domains of application grouped into 5 main categories. Table 10 presents the categories identified in the selected studies. Below are the category descriptions:  
* Suspicious Transaction Detection (CD1): Category that covers approaches that seek to identify suspicious transactions by applying different methodologies or techniques. * Money Laundering Pattern/Group/Anomaly Detection (CD2): A category that covers approaches that act by detecting and/or classifying patterns or performing money laundering-focused clusters. * Risk Assessment/Analysis (CD3): Covers approaches that apply money laundering risk rating techniques by conducting assessments or analyzes. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Studies** \\ \hline
**CD2**: Pattern detection/groups/money laundering anomalies & [52], [57], [50], [51], [52], [53], [54], [55], [56], [57], \\
**CD3**: Risk Assessment/Analysis & [58], [59], [53], [54], [57], [58], [59] \\ \hline
**CD4**: Security, control, structuring and/or governance applications & [53], [54], [52], [53], [55], [56] \\ \hline
**CD5**: Visual Analysis/Applications of Visual Techniques & [51], [54], [55], [56], [57], [58] \\ \hline \end{tabular}
\end{table} 
Table 10: Application domain categories. \begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **Study Quality Assessment Question** & **Yes** & **Partially** & **No** \\ \hline Q1 & Are the aims and objectives of the study clearly specified? & 59 (83.1\%) & 12 (16.9\%) & 0 (0\%) \\ \hline Q2 & Is the context of the study clearly stated? & 7 (9.9\%) & 34 (47.9\%) & 30 (42.3\%) \\ \hline Q3 & Does the research design support the aims of the study? & 8 (11.3\%) & 36 (50.7\%) & 27 (38.0\%) \\ \hline Q4 & Has the study an adequate description of the data analysis? & 8 (11.3\%) & 23 (32.4\%) & 40 (56.3\%) \\ \hline Q5 & Is there a clear statement of findings and was sufficient data provided to support them? & 17 (23.9\%) & 36 (50.7\%) & 18 (24.4\%) \\ \hline Q6 & Do the researchers critically examine their potential bias and influence to the study? & 2 (2.8\%) & 9 (12.7\%) & 60 (84.5\%) \\ \hline Q7 & Are the limitations of the study discussed explicitly? & 3 (4.2\%) & 14 (19.7\%) & 54 (76.1\%) \\ \hline \end{tabular}
\end{table} 
Table 9: Study quality assessment questions. * Security, control, structuring and/or governance applications (CD4): Covers approaches that apply governance, security, structuring or control techniques focused on money laundering. * Visual Analysis/Applications of Visual Techniques (CD5): Category of approaches involving applications of techniques, methodologies or visual systems focused on money laundering. Of the selected works, we can highlight that 52.1% (37 publications) have as their main focus the detection of suspicious transactions through the application of different methodologies or techniques, such as machine learning techniques, classification, clustering and others. That is, it is clear that it is an application domain with higher trends of studies by researchers compared to other application domains. 16 of the papers focus on the detection of money laundering patterns/groups/anomalies, where they act by detecting and / or classifying patterns (e.g., behavioral patterns, anomalies) or grouping (e.g., grouping transactions with similar attributes or behaviors or based on social networks, grouping of individuals or participants by behavior or roles) focused on money laundering. With regard to risk analysis and assessment, 6 of the papers have this application domain as their main focus, applying money laundering risk rating techniques, performing analyzes as risk classification systems (risk-score) and others. About the CD4 category, 6 papers apply governance, security, structuring (e.g., systems and environment, fragmented data) or control (e.g., preventive controls) techniques focused on money laundering.