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: 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.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 £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.

Purpose: The purpose of this paper is to perform a comparative study of prediction performances of an artificial neutral network (ANN) model against a linear prediction model like a linear discriminant analysis (LDA) with regards to forecasting corporate credit ratings from financial statement data.Design/methodology/approach: The ANN model used in the study is a fully connected back‐propagation model with three layers of neurons. The paper uses a comparative approach whereby two prediction models – one based on ANN and the other based on LDA are developed using identically partitioned data set.Findings: The study found that the ANN model comprehensively outperformed the LDA model in both training and test partitions of the data set. While the LDA model may have been hindered by omitted variables; this actually lends further credence to the ANN model showing that the latter is more robust in dealing with missing data.Research limitations/implications: A possible drawback in the model implementation probably lies in the selection of the various accounting ratios. Perhaps future replications of this study should look more carefully at choosing the ratios after duly addressing the problems of collinearity and duplications more rigorously.Practical implications: The findings of this study imply that since ANN models can better deal with complex data sets and do not require restraining assumptions like linearity and normality, it may be overall a better approach in corporate credit rating forecasts that uses large financial data sets.Originality/value: This study brings out the effectiveness of non‐linear pattern learning models as compared to linear ones in forecasts of financial solvency. This goes on to further highlight the practical importance of the new breed of computational tools available to techno‐savvy financial analysts and also to the providers of corporate credit.

Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting – edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This analysis is done over a variety of cost ratios (Type I Error cost: Type II Error cost) and prediction intervals as these differ depending on the situation. The results show that decision trees and survival analysis models have good prediction accuracy that justifies their use and supports further investigation