This paper analyzes 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.

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high-profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non-parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.

Financial illiteracy is widespread amongst the elderly. Financially illiterate people are more likely to experience asset loss and outlive their savings after retirement. This paper measures financial literacy of elderly Australians using Item Responses Theory. Using a Lasso regression, we find that younger, married males with higher income and greater net wealth are more likely to be financially literate. Better financial literacy is also associated with good health, higher educational attainment, better occupation and outright home ownership. Our findings suggest policy makers take action and we make informed and practicable policy recommendations.

Purpose: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.Design/Methodology/Approach: 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. Findings:The literature on money laundering can be classified into the following six broad areas: (i) anti-money laundering framework and its effectiveness, (ii) the effect of money laundering on other fields and the economy, (iii) the role of actors and their relative importance, (iv) the magnitude of money laundering, (v) new opportunities available for money laundering and (vi) 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.Originality/Value: 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 80 billion pounds 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.

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