# Fraud Audit Based on Visual Analysis: A Process Mining Approach
## 2 Background and Related Works
### Process Mining and Audit
Later, in [57], the same author combines the patterns detected by the process mining with the transactions' monetary values (transaction values), thus making it possible to prioritize all instances based on the risk score and threshold. The proposed risk assessment framework using process mining improves audit efficiency regarding the examination of process instances that present potential internal control violations. The proposals, however, are limited to specific accounting cycles and depend on a prior formalization of the categories and sub-categories to classify the detected patterns (such as those presented in [58] for the evaluated process) and on the prior knowledge of the fraud schemes, which complicates generalizing them to other business environments. In [59], [PERSON] proposes a five-step, mining-based methodology for information system audits and validates it by implementing a case study. Its main contribution is that the methodology integrates expert system techniques and risk management to perform audits of information systems using process mining. The approach recommends categorizing the deviations so as to assign them the right priority, as in previous proposals (e.g., [39]). Disadvantages include the fact that domain experts are needed to formulate the criteria required to estimate the severity of the non-conformance; this requires the prior existence of \"de jure models\", which, unfortunately, are not always available. Further, the method does not include the visual possibilities of process mining with respect to increasing the effectiveness of audits. [PERSON] and [PERSON] in [60] combine the red flag approach with process mining to reduce the number of false positives in fraud detection. Nevertheless, the use of process mining is limited to visualizing fraudulent process instances with the corresponding red flags; therefore, it does not use the visual capabilities of process mining to detect previously unknown fraud patterns or patterns not easily identifiable through traditional mechanisms. Finally, in [61], an algorithm is proposed that combines the perspectives of flow control and financial audit data. The proposal, which integrates the results obtained from previous works [62; 63; 64; 65], allows discovering process models on a colored Petri net (CPN) enriched with financial information appertaining to the process. As an alternative, in [66], [PERSON] uses data dependencies related to the accounting structure of recorded events to discover the control flow rather than the temporal order of the events. It is important to point out that this is done from a financial perspective, framing its field of action in ERP or similar solutions, thus limiting its scope. Moreover, the algorithm is not applicable to highly variable business processes. Complementing previous research, the author recently presented a new way of visualizing process mining results specifically for financial audits, in an aggregate manner, as materiality maps. Such maps provide an overview of an organization's identified processes and indicate which business processes should be considered for audits [67].