مطالب مرتبط با کلیدواژه

fraud detection


۱.

Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm(مقاله علمی وزارت علوم)

کلیدواژه‌ها: financial reporting fraud fraud detection Genetic Algorithm Data mining

حوزه‌های تخصصی:
تعداد بازدید : ۴۸۶ تعداد دانلود : ۱۹۴
both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.
۲.

Comparing the Prediction Power of Logit Regression Model and LightGBM Algorithm in Credit Card Fraud Detection(مقاله علمی وزارت علوم)

نویسنده:

کلیدواژه‌ها: fraud detection Financial Institution Credit card Logit LightGBM Machine Learning

حوزه‌های تخصصی:
تعداد بازدید : ۱۸ تعداد دانلود : ۱۳
Relying on the Area Under the Curve (AUC) measure, we compare the performance of the Logit regression model and the LightGBM algorithm. Despite these methods being common in the literature, our study emphasizes the role of statistical inference to evaluate and compare the results comprehensively. We use the training set of the Vesta (2018) dataset, provided by Vesta—a global fraud prevention company headquartered in the United States specializing in payment solutions and risk management. Originally released as part of a Kaggle competition focused on credit card fraud detection, this dataset comprises diverse transaction records, representing a rich source for exploring advanced fraud detection methods. Our analysis reveals that while the LightGBM algorithm generally yields higher predictive accuracy, the differences between the calculated AUCs of the two methods are not statistically significant. This underscores the importance of using inferential techniques to validate model performance differences in fraud detection.