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Corporate crimes in Malaysia are increasing each year. These issues are bothersome to the investors, creditors and the public as a whole because of the huge impact on all of them. Employees lose their jobs, investors do not get optimal return on their investments and creditors are unable to get their payments, and as a result, the public lose their faith on the legislation. The purpose of this study is to analyze the cases charged under Securities Commission and Bank Negara Malaysia.

This study analyzes the cases in Securities Commission and Bank Negara under four criteria which are the corporate profiles, details on crime committed, perpetrators profile and, finally, the offence.

The findings show that top-level management, especially the directors, usually commit such crime and many of them are male.

This study looks into the criteria of the cases charged under both institutions, Securities Commission and Bank Negara, which can be used to create awareness among the organizations in Malaysia.

This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in Malaysia.

Based on the concepts of ANN, a mathematical model was developed to compare non-fraud and fraud companies selected from among small market capitalization companies in Malaysia; the fraud companies had already been charged by the Securities Commission for falsification of financial statements. Ten financial ratios are used as fraud risk indicators to predict fraudulent financial reporting using ANN.

The findings indicate that the proposed ANN methodology outperforms other statistical techniques widely used for predicting fraudulent financial reporting.

The study is one of few to adopt the ANN approach for the prediction of financial reporting fraud.