A business process mining application for internal transaction fraud mitigation

Jans, Mieke; Werf, Jan Martijn E. M. van der; Lybaert, Nadine; Vanhoof, Koen

doi:10.1016/j.eswa.2011.04.159

Cited by 149 publications

(104 citation statements)

References 14 publications

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“…This discipline emerged in the context of business processes, but it has expanded to multiple areas as reported in Mans et al (2008), Poggi et al (2013), van der Aalst et al (2007), Jans et al (2011). However, the application of this set of techniques to the context of robot missions is still unexplored although it looks really promising.…”

Section: Process Miningmentioning

confidence: 99%

“…This discipline has been successfully applied to industries, organizations and public services. Some cases of use are the management of healthcare processes in hospitals (Mans et al 2008), the analysis of customer behavior in web commerce (Poggi et al 2013), the management of government offices (van der Aalst et al 2007), and the detection of transaction fraud at early stages (Jans et al 2011). Nevertheless, process mining is not extensively used in robotics, despite its potential in multi-robot missions.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing and improving multi-robot missions by using process mining

et al. 2017

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Multi-robot missions can be compared to industrial processes or public services in terms of complexity, agents and interactions. Process mining is an emerging discipline that involves process modeling, analysis and improvement through the information collected by event logs. Currently, this discipline is successfully used to analyze several types of processes, but is hardly applied in the context of robotics. This work proposes a systematic protocol for the application of process mining to analyze and improve multi-robot missions. As an example, this protocol is applied to a scenario of fire surveillance and extinguishing with a fleet of UAVs. The results show the potential of process mining in the analysis of multi-robot missions and the detection of problems such as bottlenecks and inefficiencies. This work opens the way to an extensive use of these techniques in multi-robot missions, allowing the development of future systems for optimizing missions, allocating tasks to robots, detecting anomalies or supporting operator decisions.

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Section: Process Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing and improving multi-robot missions by using process mining

et al. 2017

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“…Few approaches exist that aim to identify and/or assess process risks [7,8,21]. Wickboldt et al proposed a framework that uses process execution data for risk assessment [21].…”

Section: Related and Previous Workmentioning

confidence: 99%

“…Our approach avoids subjective opinions and learns such values from historic event data. Jans et al [8] proposed using process mining for the identification of one particular type of risk (transactional fraud risk) and showed that available process mining tools can help auditors detect fraud. By contrast, our approach focuses on quantifiable values such as delays or product quality and it emphasises automatable techniques for risk identification that can be used for run-time operational support [16].…”

Section: Related and Previous Workmentioning

confidence: 99%

Profiling Event Logs to Configure Risk Indicators for Process Delays

Pika

Aalst

Fidge

et al. 2013

Advanced Information Systems Engineering

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Abstract. Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators (PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.

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“…As such, this form of auditing can provide more assurance. However in order to automate the process of auditing [4,7,16], it first requires the formalization of the boundaries in terms of the execution data, and secondly, it can only use context data if an automated link is provided from the execution data to the context data. As the boundaries are typically formalized in terms of the context data, and as the automated link from execution data to context data is typically not available, continuous auditing is restricted in its use.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Compliance Checking

Werf

Verbeek

Aalst

2012

Lecture Notes in Computer Science

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Abstract. Organizations face more and more the burden to show that their business is compliant with respect to many different boundaries. The activity of compliance checking is commonly referred to as auditing. As information systems supporting the organization's business record their usage, process mining techniques such as conformance checking offer the auditor novel tools to automate the auditing activity. However, these techniques tend to look at process instances (i.e., cases) in isolation, whereas many compliance rules can only be evaluated when considering interactions between cases and contextual information. For example, a rule like "a paper should not be reviewed by a reviewer that has been a co-author" cannot be checked without considering the corresponding context (i.e., other papers, other issues, other journals, etc.). To check such compliance rules, we link event logs to the context. Events modify a pre-existing context and constraints can be checked on the resulting context. The approach has been implemented in ProM. The resulting context is represented as an ontology, and the semantic web rule language is used to formalize constraints.

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A business process mining application for internal transaction fraud mitigation

Cited by 149 publications

References 14 publications

Analyzing and improving multi-robot missions by using process mining

Analyzing and improving multi-robot missions by using process mining

Profiling Event Logs to Configure Risk Indicators for Process Delays

Context-Aware Compliance Checking

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