Fraud is a primary source of organization losses, amounting to up to 5% of yearly revenues. Process-based fraud (PBF) is fraud involving a deviation from the standard operating procedure (SOP) of business processes. PBF hinders the achievement of business objectives because business processes operationalize organizational strategies. A systematic content analysis of the literature was conducted on fraud detection metrics in business processes. The current state of fraud detection was surveyed by focusing on PBF metrics while including all relevant conceptual perspectives of PBF detection. The findings indicate that a large body of research has examined detection metrics for possible fraud, but less attention has been paid to PBF. In addition, the currently available PBF detection metrics do not adequately address the needs of different conceptual perspectives on business processes. For example, metrics may be undefined for one or more of the following: components of a business process, business process perspectives, critical information for auditing the business process, and business process presentation layers. This paper addresses these gaps by paying attention to PBF and various conceptual perspectives for a successful PBF detection approach. INDEX TERMS Business process fraud, fraud detection, fraud indicators, fraud metrics, process-based fraud, systematic literature review.
A business process is a set of connected events, activities, and decision points, including actors and objects, which collectively produce a beneficial outcome for the customer. The success of an organization's strategic goals and performance depends on how well these business processes are implemented and executed. However, process-based fraud (PBF), a type of fraud that occurs in business processes, can be an obstacle to achieving this. Literature analysis shows that to date PBF detection metrics have not been sufficiently addressed. Specifically, there is overlap, confusion, and no standard for fraud definitions and categories that can affect our understanding of fraud mechanisms. This study develops a taxonomy to expose the dimensions, characteristics, and objects of PBF detection and to determine their relationships by using the design science research methodology. The developed taxonomy identifies four PBF dimensions with the following characteristics: (1) process perspective {time, function, data, resource, and location}, (2) presentation layer {process map, process stream, process model, process instance, and process activity}, (3) fraud data scheme {anomalous, discrepant, missing, and wrong}, and (4) fraud domain {generic and specific}. The objective of this taxonomy is to offer a useful tool to anyone seeking to classify, develop, and evaluate PBF detection metrics, along with a holistic view of PBF detection and the determination of its borders. Additionally, it may help in standardizing the concepts of PBF detection metrics to ensure consistency between stakeholders.INDEX TERMS Business process fraud, fraud categories, fraud classification, fraud detection, fraud indicators, fraud metrics, fraud symptoms, fraud taxonomy, process-based fraud (PBF), red flags.
Theories of information systems (IS) can be categorized into five types: analytic, explaining, prediction, explaining and prediction, and design and action theory. A taxonomy could be considered a type of analysis theory which specifies the dimensions and characteristics of objects of interest by defining their shared features. Developing a taxonomy can be well suited to Design Science Research (DSR), since the primary goal of DSR is to develop an artifact. DSR is a scientific method that attempts to combine knowledge about the design and development of a solution to enhance existing systems, solve problems, and create a new artifact, such as a taxonomy. Taxonomy is crucial for understanding any phenomenon. It provides a holistic view of that phenomenon, and the classification of objects helps researchers and practitioners to understand complex domains. Nickerson, Varshney and Muntermann offered a method to develop a taxonomy based on well-established literature. Their method considered the only well-established taxonomy development method in the IS discipline. However, the literature reveals that the taxonomy development process in IS research often remains vague and taxonomies are rarely evaluated. This paper aims to improve the taxonomy development method by adopting comprehensive steps from DSR. This includes developing an integration framework for all forms of reasoning logic that are used for developing taxonomy components. The improved method supports creativity by including abduction as a reasoning logic. It also facilitates the efforts of developing a taxonomy for novice researchers by providing a complete taxonomy development roadmap.
Occupational fraud is defined as the deliberate misuse of one's occupation for personal enrichment. It poses a significant challenge for organizations and governments. Estimates indicate that the funds involved in occupational fraud cases investigated across 125 countries between 2018 and 2019 exceeded US$3.6 billion. Process-based fraud (PBF) is a form of occupational fraud that is perpetrated inside business processes. Business processes underlie the logic of the work that organizations undertake, and they are used to execute an organization's strategies to achieve organizational goals. Business processes should be examined for potential fraud risks to ensure that businesses achieve their objectives. While it is impossible to prevent fraud entirely, it must be detected. However, PBF detection metrics are not well developed at present. They are scattered, unstandardized, not validated, and, in some cases, absent. This study aimed to develop a comprehensive PBF detection metric by leveraging and operationalizing a taxonomy of fraud detection metrics for business processes as an underlying theory. 41 PBF detection metrics were deduced from the taxonomy using design science research. To evaluate their utility, the application of the metrics was undertaken using illustrative scenarios, and a real example of the implementation of the metrics was provided. The developed metrics form a complete, classified, validated, and standardized list of PBF detection metrics, which include all the necessary PBF detection dimensions. It is expected that the stakeholders involved in PBF detection will use the metrics established in this work in their practice to increase the effectiveness of the PBF detection process.
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