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Abstract ▼ AI-Helper

The empirical studies on internal control have reported the positive effects of intemal control system to improve the quality of financial reporting and minimize corporatc fraud, however they have been concerned that the audit fees have largely increased. The objective of this study is to develop ...

The empirical studies on internal control have reported the positive effects of intemal control system to improve the quality of financial reporting and minimize corporatc fraud, however they have been concerned that the audit fees have largely increased. The objective of this study is to develop internal control assessment models to cope with such problems by applying data mining techniques including decision trec(DT), neural networks(NN), and support vector machine(SVM). This study also offers comparative statistics on the performance of those models in the respect of prediction accuracy on internal control weakness(ICW). Empirical results with ICW prediction on Korea firms indicate that the prediction accuracy of DT, NN and SVM is 76.7%, 77.9%, and 79.6%, respectively. The results of T-test show that the prediction accuracy of each model is significantly different at 1% level. This study demonstrates that data mining techniques can be applied to the implementation of internal control assessment models with high accuracy.

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