A framework for internal fraud risk reduction at it integrating business processes : the IFR² framework

Fraud is a million dollar business and it is increasing every year. Both internal and external fraud present a substantial cost to our economy worldwide. A review of the academic literature learns that the academic community only addresses external fraud and how to detect this type of fraud. Little or no effort to our knowledge has been put in investigating how to prevent ánd to detect internal fraud, which we call ‘internal fraud risk reduction’. Taking together the urge for research in internal fraud and the lack of it in academic literature, research to reduce internal fraud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR² framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.El fraude es un negocio millonario y está aumentando cada año. Tanto el fraude interno como el externo presentan un coste considerable para nuestra economía en todo el mundo. Este artículo sobre la literatura académica enseña que la comunidad académica solo se dirige al fraude externo, y cómo se detecta este tipo de fraude. Que sepamos, se ha hecho poco o ningún esfuerzo en investigar cómo evitar y detectar el fraude interno, al que llamamos ‘reducción del riesgo de fraude interno’. Teniendo en cuenta la urgencia de investigar el fraude interno, y la ausencia de ello en la literatura académica, la investigación para reducir este tipo de fraude es esencial. Este tema puede ser aún investigado con mayor profundidad solo después de tener un marco, en el que implementar investigación empírica. En este artículo, presentamos el marco IFR, deducido tanto de la literatura académica como de las prácticas empresariales actuales, donde el foco del marco sugiere usar un enfoque de extracción de datos

Neural networks applied to analytical procedures.

Both U.S. and international standards require auditors to perform analytical procedures during the audit planning phase to assess the risk of material misstatements in the financial statements and, near the end of the audit, to determine whether the financial statements are consistent with the auditor’s understanding of the entity. It is also permissible to use analytical procedures as a substantive procedure. A key step underlying the application of analytical procedures is the formation of a precise expectation, which subsequently affects the effectiveness of analytical procedures. The type of analytical procedure techniques used to develop expectation models is left to the auditor’s discretion. According to auditing standards, it can be any technique ranging from simple comparisons of items to sophisticated analytical models. However, in a data-rich environment, the effectiveness of traditional, simple, analytical procedure methods has been recently questioned, and more advanced approaches have been called for. Among modern statistical and machine-learning methods, neural networks have proved to be useful in both pattern recognition and prediction. Coakley and Brown’s (1993) study was the first to research the application of neural networks as an analytical procedure to direct auditors’ attention. Following their recommendations, the current study extends their work by incorporating input data obtained from both audited, periodic financial statements of multiple firms and exogenous variables to study analytical procedure techniques of varying levels of sophistication. This study used an experimental design to examine the relative effectiveness of two well-documented analytical review techniques (ratio analysis and regression analysis) and an alternative approach, artificial neural networks. Archival data were obtained from seven listed Chinese companies operating in the dairy industry in order to train and test alternative techniques. The methodology for the study is discussed in detail in five subsections. Results suggest that the neural network approach was not significantly more effective than financial ratios and regressions, and none of the three approaches provided more overall effectiveness than a purely random procedure. However, the neural network approach did yield considerably fewer Type II errors than the other methods