Detection of fraudulent transactions using artificial neural networks and decision tree methods

Abstract

The accounting systems generate a large amount of data due to financial transactions. Intentionally fraudulent transactions can occur in high-dimensional and large numbers of emerging data. While many methods can be used for the estimation and detection of fraudulent transactions in accounting, which differ in the audit process, scope and application method, data mining methods can also be used today due to a large number of data and the desire not to narrow the scope of the audit. This study tested the accuracy of detecting fraudulent transactions using artificial neural networks and decision tree methods. According to the results of the analysis test data set for detecting fraud or error risk, 99.7981% accuracy was obtained in the artificial neural networks method and 99.9899% in the decision tree method.

The Moderating Effect of Auditor Size in the Relationship between Using Artificial Intelligence Techniques and Fraud Risk Assessment

“…These findings demonstrate the significance of ANN technique, which have a positive and strong correlation with fraud risk assessment, which means that ANNs are widely used in risk assessment and fraud detection because they are simple to handle larger sets of data and despite their complex algorithms, yield results that are easily interpretable. This finding aligns with numerous earlier research studies, such as (Işık et al, 2023;Murorunkwere et al, 2022;López et al, 2019). Furthermore, the results show a significant positive relationship between machine learning and fraud risk assessment, with (T) value reaching 3.583, which is greater than the tabular value and significant at the (Sig = 0.010) levels.…”

“…Artificial Neural Networks (ANN) are defined as a collection of algorithms that use approaches similar to those found in the human brain (Murorunkwere et al, 2022). ANN algorithms are used by (Işık et al, 2023) to test the accuracy of detecting fraudulent transactions and achieved 99.7981% accuracy in detecting fraud. As stated in (López, et al, 2019), ANN make it easy to handle larger datasets and despite their complex algorithms, they produce easily interpretable results, which makes it popular in risk assessment and fraud detection.…”