论文标题
信用卡欺诈检测 - 分类器选择策略
Credit card fraud detection - Classifier selection strategy
论文作者
论文摘要
机器学习开发了用于财务欺诈检测的新工具。使用带注释的交易样本，机器学习分类算法学会了检测欺诈。随着信用卡交易量的不断增长和欺诈百分比的增加，人们越来越有兴趣找到适当的机器学习分类器进行检测。但是，欺诈数据集是多种多样的，并且表现出不一致的特征。结果，在给定数据集上有效的模型不能保证在另一个数据集上执行。此外，随着时间的推移，数据模式和特征的时间漂移的可能性很高。此外，欺诈数据具有巨大的不平衡。在这项工作中，我们将采样方法评估为可行的预处理机制，以处理失衡并提出一个数据驱动的分类器选择策略，以实现高度不平衡的欺诈检测数据集。基于我们的选择策略得出的模型超过了同行模型，同时在更现实的条件下工作，建立了策略的有效性。
Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, fraud data sets are diverse and exhibit inconsistent characteristics. As a result, a model effective on a given data set is not guaranteed to perform on another. Further, the possibility of temporal drift in data patterns and characteristics over time is high. Additionally, fraud data has massive and varying imbalance. In this work, we evaluate sampling methods as a viable pre-processing mechanism to handle imbalance and propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets. The model derived based on our selection strategy surpasses peer models, whilst working in more realistic conditions, establishing the effectiveness of the strategy.
