Accounting systems are integral to managing financial transactions, and they generate substantial volumes of data as a result. This vast amount of data can create environments conducive to intentional fraudulent activities, particularly in high-dimensional settings where the complexity and volume of information can obscure irregularities. To combat this issue, various methods have been developed to estimate and detect fraudulent transactions within accounting systems. These methods differ widely in their audit processes, scopes, and applications, reflecting the diverse challenges faced in financial oversight.

In recent years, data mining techniques have gained prominence as effective tools for detecting fraud. Their utility stems from the ability to handle large datasets while maintaining a comprehensive audit scope, which is essential for identifying potential anomalies. This study investigated the effectiveness of two specific data mining approaches: artificial neural network and Random Forest methods. Utilizing a dataset comprising 10,000 entries, this study aimed to evaluate how well these methods could detect fraudulent transactions.

The analysis of the test dataset yielded impressive results, with the artificial neural network method achieving an accuracy rate of 90%. Meanwhile, the Random Forest method outperformed it, achieving an accuracy rate of 96.30% in identifying risks associated with fraud or errors. These findings underscore the potential of advanced data mining techniques in enhancing the integrity of accounting systems and improving fraud detection capabilities.