Search

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]