# Fraud Audit Based on Visual Analysis: A Process Mining Approach
## 2 Background and Related Works
### Process Mining and Visual Analytics  
Process mining is an applied science situated between computational intelligence and data mining on the one hand, and between modeling and process analysis on the other [34]. It consists of a set of techniques that act on event logs for discovery, monitoring, and process improvement. The premise to be able to apply this technique is that it must be possible to associate sequences of activities that make up the event log with instances of the process. The instance or case is the entity that is modified by the process, while the trace represents the unique sequence of activities undergone during each instance [35].  
There are three types of process mining: discovery, which enables building a directed graph automatically, using the process operations' ordered sequence (its evolution was analyzed by [PERSON] in [36]); conformance checking, which evaluates the conformity between the discovered model, the actual behavior of the operations registered in the traces, and the process's regulated model [37; 38]. Conformance checking can be used to compare real process executions with a normative model; however, the outcome of current conformance checking techniques is too vast for an auditor to inspect further [39]. Finally, enhancement facilitates the extension of the process model in relation to contextual information stored in the traces. This makes it possible to identify optimization points and introduce changes that increase the efficiency of the process. A related case study is presented in [40].  
By using the input elements, process mining offers the possibility of covering various perspectives that are vital for audit analysis [41]: The control-flow perspective, which is used to analyze the order in which activities are executed; the organizational perspective, which examines the behavior of actors (originators) and their relationship during the execution of the process; the case perspective, which focuses on the analysis of the particular behavior of attributes at each instance. Some proposals also include the time perspective, which focuses on the duration and frequency of occurrence of events [42].  
Several recent studies have emphasized the use of visual analytics to explore and understand large event logs as a major process mining challenge [34]. In [43], visual analytics focus on the combination of machine learning techniques, data mining, and visualization in order to take advantage of human capabilities for the parallel visual processing of large volumes of data and the identification of atypical patterns. The paper's author mentions that scalability and the visual aggregation of events and cases constitute a major challenge and opportunity for process analysis when using visual analytics methods.  
In [44], an approach to the categorization of process mining techniques regarding aspects of visual analysis is presented, while in [45], the general-purpose tool \"InterPreta\" is proposed to perform interactive process analysis, combining pre-existing process mining techniques with visual analytics. This tool supports exploratory analysis through different views of the process in order to identify and examine causes of deviations. This tool could be reused in the field of auditing to support an audit framework. As an added value, it makes use of the context-aware performance analysis framework proposed in [46] to provide and rank the statistically significant attributes (i.e., the process context).