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Financial statement fraud is a costly problem for society. Detection models can help, but a framework to guide variable selection for such models is lacking. A novel Fraud Detection Triangle (FDT) framework is proposed specifically for this purpose. Extending the well‐known Fraud Triangle, the FDT framework can facilitate improved detection models. Using Benford's law, we demonstrate the posited framework's utility in aiding variable selection via the element of surprise evoked by suspicious information latent in the data. We call for more research into variables that measure rationalisations for fraud and suspicious phenomena arising as unintended consequences of financial statement fraud. [ABSTRACT FROM AUTHOR]

Understanding player positional roles are important for match-play tactics, player recruitment, talent identification, and development by providing a greater understanding of what each positional role constitutes. Currently, no analysis of competition technical skill data exists by player position in the Australian Football League Women's (AFLW) competition. The primary aim of the research was to use data-driven techniques to observe what positions and roles characterise AFLW match-play using detailed technical skill action data of players. A secondary aim was to comment on the application of clustering methods to achieve more interpretable, reflective positional clustering. A two-stage, unsupervised clustering approach was applied to meet these aims. Data cleaning resulted in 165 variables across 1296 player seasons in the 2019–2022 AFLW seasons which was used for clustering. First-stage clustering found four positions following a common convention (forwards, midfielders, defenders, and rucks). Second-stage clustering found roles within positions, resulting in a further 13 clusters with three forwards, three midfielders, four defenders, and three ruck positional roles. Key variables across all positions and roles included the field location of actions, number of contested possessions, clearances, interceptions, hitouts, inside 50s, and rebound 50s. Unsupervised clustering allowed the discovery of new roles rather than being constrained to pre-defined existing classifications of previous literature. This research assists coaches and practitioners by identifying key game actions players need to perform in match-play by position, which can assist in player recruitment, player development, and identifying appropriate match-play styles and tactics, while also defining new roles and suggestions of how to best use available data. [ABSTRACT FROM AUTHOR]

Operational risks are increasingly prevalent and complex to manage in organisations, culminating in substantial financial and non-financial costs. Given the inefficiencies and biases of traditional manual, static and qualitative risk management practices, research has progressed to using data analytics to objectively and dynamically manage risks. However, the variety of operational risks, techniques and objectives researched is not well mapped across industries. This paper thoroughly reviews the emerging research area applying data analytics to operational risk management (ORM) within financial services (FS) and energy and natural resources (ENR). A systematic literature search resulted in 2,538 publications, from which detailed bibliometric and content analyses are performed on 191 studies of relevance. The literature is classified using a novel multi-layered framework, informing critical analyses of the analytics techniques and data employed. Five core themes emerge, relevant to practitioners, researchers, educators and students across any sector: risk identification, causal factors, risk quantification, risk prediction and risk decision-making. Generally, ENR studies focus on identifying causal factors and predicting specific incidents, whereas FS applications are more mature surrounding risk quantification. To conclude, the comprehensive review reveals areas where further research is needed to advance ORM within and beyond FS and ENR, in pursuit of improved decision-making. [ABSTRACT FROM AUTHOR]