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In the field of Financial Technology, machine learning provides important support for decision-making through the effective use of data. Credit card fraud detection technology is a good example, but it still faces two challenges: the unbalanced data sets and cost-sensitive characteristics. In this paper, we proposed an enhanced CSat (Customer Satisfaction)-related AdaBoost. Based on the traditional AdaBoost, we consider the expected loss of the impact of customer satisfaction and re-adjust the weight of different categories in the cost adjustment function of the basic classifier. Considering the serious consequences of fraud transactions, we also implemented a metric related to the Total Profit of Classification (TPC) to evaluate performance. The results show that the CSat-related AdaBoost performed better in F1-score and AUC score compared to the traditional AdaBoost and some mainstream models, the reliability and interpretability of TPC as an evaluation metric is also demonstrated in our paper.
Yang et al. (Wed,) studied this question.