detecting financial fraud using machine learning techniques

Financial fraud detection is a challenging problem due to four primary reasons: the constantly changing fraudulent behavior, the lack of a mechanism to track fraud data, the specific limitations of available detection techniques (such as machine learning algorithms), and the highly dispersed financial fraud dataset. thus, it can be declared that teaching algorithms are complex. the current study used machine learning techniques, including support vector machine regression and boosted regression tree, to detect financial fraud in the iranian stock market. the findings indicated that the boosted regression tree machine model has the lowest rmse. furthermore, concerned with the sensitivity value of the models, the boosted regression tree model has the highest sensitivity in the sense that they had correctly detected the absence of financial fraud tehran stock exchange market the tehran stock exchange market. the boosted regression tree has the highest kappa coefficient indicating the appropriate performance of this model compared to other models used in the research.