Applying Machine Learning to Audit Data: Enhancing Fraud Detection, Risk Assessment and Audit Efficiency

Machine learning (ML) is used globally as a tool for predictive analysis. Within auditing, the use of audit data helps to uncover fraud indicators, identify risk areas and implement predictive models for continuous audit monitoring. Researchers are using various machine learning methods to analyze large and complex audit data to facilitate prediction. In this study, an online UCI dataset of 776 lines and 27 features is used. Out of these 27 features, 13 are eliminated due to their low impact on the target dataset or due to the 'important feature selection' algorithm. In this analysis, I used supervised learning methods, namely K-Nearest Neighbors, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, Linear Discriminant Analysis, Gaussian Naive Bayes, Extra Tree Algorithm, Gradient Boosting Algorithm, Ada Boosting Algorithm and XGBoost Algorithms. The experimental results highlight the power of eight neighbor KNN and evaluate its effectiveness, sensitivity, precision, accuracy and F1 score in comparison with other methods such as Naive Bayes, SVM (Linear Kernel), Decision Tree Classifier and Random Forest Classifier.