基于实例代价敏感学习的转账欺诈检测
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
摘要：
信用卡交易欺诈是一个影响全球持卡人的日益严重的问题。金融机构越来越依赖数据驱动的方法来开发欺诈检测系统，这些系统能够自动检测和阻止欺诈交易。从机器学习的角度来看，检测欺诈交易的任务是一个二进制分类问题。分类模型通常根据统计性能指标进行训练和评估，如似然性和AUC。然而，这些措施没有考虑到实际的商业目标，即尽量减少欺诈造成的财务损失。欺诈检测被认为是一个依赖于实例的成本敏感的分类问题，其中由于误分类的成本在实例之间变化，并且需要适应的方法来学习分类模型。在这篇文章中，一个依赖于实例的阈值推导出，基于实例依赖的成本矩阵转移欺诈检测，允许为每笔交易的最佳成本为基础的决策。提出了两种新的分类器，基于lasso正则化逻辑斯谛回归和梯度树提升，直接最小化建议的实例相关的成本措施时，学习分类模型。所提出的方法在R软件包cystem it和csboost中实现，并在来自机器学习竞赛网站Kaggle的公开数据集和专有卡交易数据集上与最先进的方法进行比较。实验结果突出了通过采用所提出的方法减少欺诈损失的潜力。
英文摘要：
Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting fraudulent transactions is a binary classification problem. Classification models are commonly trained and evaluated in terms of statistical performance measures, such as likelihood and AUC, respectively. These measures, however, do not take into account the actual business objective, which is to minimize the financial losses due to fraud. Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model. In this article, an instance-dependent threshold is derived, based on the instance-dependent cost matrix for transfer fraud detection, that allows for making the optimal cost-based decision for each transaction. Two novel classifiers are presented, based on lasso-regularized logistic regression and gradient tree boosting, which directly minimize the proposed instance-dependent cost measure when learning a classification model. The proposed methods are implemented in the R packages cslogit and csboost, and compared against state-of-the-art methods on a publicly available data set from the machine learning competition website Kaggle and a proprietary card transaction data set. The results of the experiments highlight the potential of reducing fraud losses by adopting the proposed methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
