ABSTRACT Recent headlines indicate that the Big Four accounting, auditing, and professional services firms (Big Four) have invested billions in artificial intelligence (AI), as they digitally transform their firms. As part of that analysis, this paper reviews several large language model (LLM) applications that have been developed and considers some of the implications of those applications, such as the democratization of knowledge and the potential impact on barriers to entry. Because LLMs are early in their life cycle and there is limited information about those applications, I take a demonstration‐based field work approach, gathering information from firm demonstrations and then building some prototypes in LLMs that simulate the demonstrated capabilities.
ABSTRACT This study aims to enhance bankruptcy prediction models by employing the gradient‐boosting TreeNet algorithm. This research assesses the predictive accuracy of TreeNet in bankruptcy classification using a high‐dimensional approach. Specifically, it categorizes bankruptcy as nonbankrupt, Chapter 7, or Chapter 11 categories. This research utilizes a large dataset comprised of 76,069 firm‐year observations for the years between 1991 and 2019. The findings highlight the high classification accuracy of TreeNet, particularly in predicting Chapter 7 cases, and its robust performance across different time horizons. The TreeNet prediction model is a valuable tool for decision‐making and risk management in finance, auditing, and policymaking sectors.
Tax authorities face growing volumes of filings and payments and must manage procedural non-compliance (e.g., late filing, late payment and the accumulation of tax arrears) with limited administrative capacity. Many existing machine learning (ML) and artificial intelligence (AI) applications in tax administration rely on binary outcomes, which limits severity-based prioritisation and the targeting of low-cost interventions. This study develops a multiclass prediction model for administrative tax compliance severity using Slovakia's public tax reliability index, which classifies companies into three categories based on regulator-defined administrative criteria. Using only financial statement ratios and governance indicators, we evaluate nine classifiers and five resampling techniques for class imbalance. Gradient boosting models (XGBoost and CatBoost) perform best, reaching an OvR AUC-ROC above 96% for 1-year forecasts, with modest declines for 2- and 3-year horizons. SHAP explanations indicate that smaller boards and indicators consistent with liquidity constraints and tax-payment pressure are associated with higher-severity administrative classes. The proposed workflow offers a transferable framework for multiclass, long-horizon compliance risk prediction and can support proactive case management (e.g., targeted reminders and payment facilitation, including payment plans, and debt prioritisation) in advance of the regulator's semi-annual updates; it may also provide researchers with a potential early-warning label of administrative compliance frictions that could be examined in relation to financial distress.
Pictures are a powerful medium to communicate complex and emotive messages. In particular, the human face expresses corporate culture including diversity and equal opportunity. However, despite the recent visual turn in accounting and finance, quantitative research on diversity in photos is scant because automated solutions for identifying and classifying human faces were not readily available. This paper seeks to bridge this gap by tailoring automated large-sample facial analysis from the recent computing literature into the accounting literature. Our automated model identifies and classifies faces with sufficient accuracy and precision to draw reliable inferences, and this model is made available for future research. We use the resulting quantitative dataset to analyse intermodal discourse in the annual report, asking the question: Do cover photos augment textual diversity disclosure, or are they PR window-dressing? Results suggest that the decision to publish faces on the annual report cover is associated with an integrated reporting strategy and high-quality diversity disclosure, consistent with pictures augmenting textual disclosures. Gender and ethnic diversity of faces in cover photos tell a different story, tending towards PR window-dressing. Methods and findings from this paper may be of interest to researchers, government and policy makers involved in diversity research and regulation.
Genetic programming (GP) is used to obtain multiperiod bankruptcy prediction models, as well as to perform a prior feature selection process for these models. Given the controversy in the field of bankruptcy prediction about the need to include (or not) variables from the economic environment as input information for the prediction models, an analysis is carried out to check whether the impact that the economic environment undoubtedly has on the firms can be captured using only the financial variables of the firm as explanatory variables. To this end, the analysis includes a study of the correlation between the estimates of the prediction models and certain economic indicators. The results confirm the possibility of capturing the evolution of the economic environment using only financial information as input, as strong correlations are shown between the predictions of the models and important economic indicators over a very long postlearning period (2008-2020) and varied in terms of the economic environment (crisis, recovery, COVID, etc.).
