# Fraud Audit Based on Visual Analysis: A Process Mining Approach
###### Abstract  
Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (dotted chart, trace alignment, fuzzy miner... ). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sectors. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach. fraud audit; process mining; visual analytics +
Footnote †: journal: Medical  
allegations. Fraud audits arise as an alternative to uncover fraud by adopting a proactive approach. A fraud audit is the application of audit procedures to a population of business transactions to increase the likelihood of identifying fraud [6]. In this context, audit analytics has emerged as an applied science focused on pattern discovery and analysis, anomaly identification, and the extraction and visualization of useful information, thus increasing the effectiveness of audits [7]. Specifically, process mining stands out as a valuable technique for audits [8; 9; 10; 11]. It has even been accurately determined as the most popular unsupervised method for external audits [12]. Its main objective is to extract knowledge from the event logs of commonly available systems and provide timely information to decision makers. Several approaches have sought to achieve automatic processing of traces, revolutionizing the traditional methods. The entire population is audited, fraud patterns that were not detectable using traditional techniques are discovered, and the consumption of resources is reduced. Nevertheless, current audit approaches present shortcomings as regards the use of the visual capabilities that support process mining techniques. State-of-the-art reviews point to the fact that process mining and visualizations require more in-depth academic research applied to audits [12]. We are not aware, however, of the existence of a formalized proposal that combines both disciplines to address fraud auditing. The present work presents an approach that combines process mining techniques with visual analytics for fraud audits. It includes a method that guides auditors in their detection of fraudulent transactions during a fraud audit. The method can be generalized to any business domain. In this respect, existing process mining algorithms were reoriented towards the domain of fraud auditing, taking advantage of the visual capabilities. Our proposal is based on a fraud audit approach. Specifically, the study \"Fraud data analytics methodology: the fraud scenario approach to uncovering fraud in core business systems\" [13] was used as a reference. Analysis techniques proposed in the approach were selected by a group of experts in fraud audits, using the Delphi method and the analytic hierarchy process (AHP).