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This paper analyses the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues.

This paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables.

The results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking.

These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.

The purpose of this study is to review the literature on money laundering and its related areas. The main objective is to identify any gaps in the literature and direct attention towards addressing them.

A systematic review of the money laundering literature was conducted with an emphasis on the Pro-Quest, Scopus and Science-Direct databases. Broad research themes were identified after investigating the literature. The theme about the detection of money laundering was then further investigated. The major approaches of such detection are identified, as well as research gaps that could be addressed in future studies.

The literature on money laundering can be classified into the following six broad areas: anti-money laundering framework and its effectiveness, the effect of money laundering on other fields and the economy, the role of actors and their relative importance, the magnitude of money laundering, new opportunities available for money laundering and detection of money laundering. Most studies about the detection of money laundering have focused on the use of innovative technologies, banking transactions or real estate- and trade-based money laundering. However, the literature on the detection of shell companies being explicitly used to launder funds is relatively scarce.

This paper provides insights into an area related to money laundering where research is relatively scant. Shell companies incorporated in the UK alone were identified to be associated with laundering £80bn of stolen money between 2010 and 2014. The use of these entities to launder billions of dollars as witnessed through the laundromat schemes and several data leaks clearly indicate the need to focus on illicit financial flows through such entities.

The authors passively apply a researcher profile pitch (RPP) template tool in accounting and across a range of Business School disciplines.

The authors document a diversity of worked examples of the RPP. Using an auto-ethnographic research design, each showcased researcher reflects on the exercise, highlighting nuanced perspectives drawn from their experience. Collectively, these examples and associated independent narratives allow the authors to identify common themes that provide informative insights to potential users.

First, the RPP tool is helpful for accounting scholars to portray their essential research stream. Moreover, the tool proved universally meaningful and applicable irrespective of research discipline or research experience. Second, it offers a distinct advantage over existing popular research profile platforms, because it demands a focused “less”, that delivers a meaningful “more”. Further, the conciseness of the RPP design makes it readily amenable to iteration and dynamism. Third, the authors have identified specific situations of added value, e.g. initiating research collaborations and academic job market preparation.

The RPP tool can provide the basis for developing a scalable interactive researcher exchange platform.

The authors argue that the RPP tool potentially adds meaningful incremental value relative to existing popular platforms for gaining researcher visibility. This additional value derives from the systematic RPP format, combined with the benefit of easy familiarity and strong emphasis on succinctness. Additionally, the authors argue that the RPP adds a depth of nuanced novel information often not contained in other platforms, e.g. around the dimensions of “data” and “tools”. Further, the RPP gives the researcher a “personality”, most notably through the dimensions of “contribution” and “other considerations”.

The pitching research template (PRT) is designed to help pitchers identify the core elements that form the framework of any research project. This paper aims to provide a brief commentary on an application of the PRT to pitch an environmental finance research topic with a personal reflection on the pitch exercise discussed.

This paper applies the PRT developed by Faff (2015, 2019) to a research project on estimating the strength of carbon pricing signals under the European Union Emissions Trading Scheme.

The PRT is found to be a valuable tool to refine broad ideas into impactful and novel research contributions. The PRT is recommended for use by all academics regardless of field and particularly PhD students to structure and communicate their research ideas. The PRT is found to be particularly well suited to pitch replication studies, as it effectively summarizes both the “idea” and proposed “twist” of a replication study.

This letter is a reflection on a research teams experience with applying the PRT to pitch a replication study at the 2020 Accounting and Finance Association of Australia and New Zealand event. This event focused on replicable research and was a unique opportunity for research teams to pitch their replication research ideas.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

The paper aims at developing a global ranking system determining a country's appeal as a destination for money laundering.

This paper uses principal component analysis (PCA), with a mix of standardised and unstandardised components relating to attractiveness, economic freedom and money laundering risk to come up with an index of money laundering appeal.

Four components relating to economic feasibility, financial liberty, government spending and tax regime are critical in influencing a country's money laundering appeal.

This paper attempts to use a standardised and replicable methodology to condense into a single measure the complex and multifaceted phenomenon of a country's appeal as a destination for money laundering, thus avoiding the difficulty associated with precisely calculating illicit financial flows.

The ranking system could be used to determine the destinations attractive for laundering money. Such information can be used to come up with more effective preventative strategies to combat phenomena responsible for the stagnation of economic growth through tax evasion, corruption and creation of non-competitive markets.

It is the first attempt to use a statistical technique to understand the underlying components of a country's money laundering appeal.

The objective of this study is to investigate the relationship between trade credit supply and financial distress outcomes, considering the role that trade credit plays as a substantial source of liquidity for distressed companies. Specifically, it examines whether there is an association between trade credit supply and the outcomes experienced by companies that undergo the voluntary administration (VA) insolvency procedure under Australian corporate law.

The study examines a sample of companies that were listed on the Australian Securities Exchange and entered VA between 2002 and 2019. Ordered logistic regression is used to determine the relation between trade credit and VA outcomes. The VA outcomes considered are as follows: (1) company liquidation, (2) orderly dissolution through an agreement with creditors, or (3) an agreement with creditors for reorganization of all or part of the company's business.

The findings show that trade creditors' willingness to supply credit is influenced by their rational expectations about the future prospects of financially distressed customers. Higher levels of trade credit and an increase in trade credit supply prior to VA are associated with a greater probability of achieving a reorganization versus a liquidation or dissolution outcome.

There is no apparent prior study investigating the connection between trade credit supply and outcomes for distressed companies entering insolvency administration. Therefore, this study provides novel evidence on the role of trade credit in the context of financial distress. Understanding the relationship between trade credit supply and outcomes is particularly significant considering that many jurisdictions offer distressed companies the opportunity to pursue reorganization under their insolvency laws. Examining financial distress and trade credit in the Australian creditor-friendly context expands on existing research. Prior research has predominantly relied on data from the United States, which has debtor-friendly bankruptcy law. Consequently, these studies may lack generalizability to jurisdictions with creditor-friendly law such as Australia.

This paper examines the moderating effect of good corporate governance on the association between internal information quality and tax savings.

This study uses a quantitative approach. It employs an Australian sample of analysis composed of 1,295 firm-year observations from the period 2017 to 2021. Data relating to corporate governance are hand-collected from the annual reports.

Based on the result of the analysis, this study demonstrates that the interaction between corporate governance and quality of internal information is positively associated with tax savings. Superior corporate governance is critical in activating the effect of internal information quality on tax savings. This finding is robust to a battery of robustness checks and additional tests.

This examination utilizes only publicly traded companies from one developed country.

For the company management, an effective governance structure must be at the top because it will determine the development of all other areas. This study emphasizes the need to continuously improve the effectiveness of corporate governance practices. For long-term investors, an important indicator that can be considered in assessing the “safety” of a company’s tax strategy is its corporate governance aspects. For regulators, this study is expected to assist regulators in creating a more adequate corporate governance implementation and disclosure package to be implemented by corporations in the future.

This study provides new evidence on a crucial construct that can strengthen the relationship between internal information quality and tax savings.