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ItemFalse Positives in Credit Card Fraud Detection: Measurement and Mitigation( 2022-01-04)Credit Card Fraud Detection is a classification problem where different types of classification errors cause different costs. Previous works quantified the financial impact of data-driven fraud detection classifiers using a cost-matrix based evaluation approach, however, none of them considered the significant financial impact of false declines. Analysts reported that fraud prediction in e-commerce still has to deal with false positive rates of 30-70%, and many cardholders reduce card usage after being wrongly declined. In our paper, we propose a new method for assessing the cost of false declines and evaluate several state-of-the-art fraud detection classifiers using this method. Further, we investigate the effectiveness of ensemble learning as previous work supposed that a combination of diverse, individual classifiers can improve performance. Our results show that cost-based evaluation yields valuable insights for practitioners and that our ensemble learning strategy indeed cuts fraud cost by almost 30%.
ItemDetecting potential money laundering addresses in the Bitcoin blockchain using unsupervised machine learning( 2022-01-04)Money laundering is a serious problem worldwide, especially in the crypto market. This is mostly because of the anonymity that many cryptocurrencies offer. That is one of the reasons why cryptocurrencies are a haven for money laundering, because it is easier for criminal entities to buy the currency and then trade it for real fiat money. Detecting money laundering in cryptocurrency can be tricky because the crypto network is large and convoluted and nearly impossible to analyze by hand. What we can do is look at addresses that took part in transactions as actors and then use machine learning to predict what addresses are possibly laundering money. In this paper we intend to analyze methods that can be used to detect money laundering in Bitcoin using machine learning to empower investigators to more accurately and efficiently determine whether a suspicious activity is money laundering.
ItemA Model for Detecting Accounting Frauds by using Machine Learning( 2022-01-04)This paper aims to develop a machine learning model that enables to predict signs of financial statement frauds by combining the domain knowledge of machine learning and accounting. Inputs of this model is a published dataset of financial statements, and outputs involve the conclusions whether the predicted financial statements indicate the signs of financial statement frauds or not. Currently, XGBoost is recognized as one of the most popular classification methods with fast performance, flexibility, and scalability. However, its default properties are not suitable for fraudulent detecting of imbalanced datasets. To overcome this drawback, this research introduces a new machine learning model based on XGBoost technique, called f(raud)-XGBoost. The proposed model not only inherits XGBoost advantages but also enables it to detect financial statement frauds. We apply the Area Under the Receiver Operating Characteristics Curve and NDCG@k to perform the evaluation process. The experimental results show that the new model performs slightly better than three existing models including logistic regression model that is based on financial ratios, Support-vector-machine model, and RUSBoost model