论文标题
交错的序列RNN用于欺诈检测
Interleaved Sequence RNNs for Fraud Detection
论文作者
论文摘要
支付卡欺诈会导致全球银行和商人造成数十亿美元的损失，通常会加剧复杂的犯罪活动。为了解决这个问题，许多实时欺诈检测系统都使用基于树的模型，要求复杂的功能工程系统有效地通过历史数据丰富交易，同时遵守毫秒级别的潜伏期。 在这项工作中，我们不需要使用经常性的神经网络并将付款视为交错序列，而每张卡的历史是无限的，不规则的子序列，我们不需要这些昂贵的功能。我们提出了一个完整的RNN框架来实时检测欺诈，提出了从预处理到部署的有效ML管道。 我们表明，这些无功能的多序列RNN优于最先进的模型，可节省数百万美元的欺诈检测和使用更少的计算资源。
Payment card fraud causes multibillion dollar losses for banks and merchants worldwide, often fueling complex criminal activities. To address this, many real-time fraud detection systems use tree-based models, demanding complex feature engineering systems to efficiently enrich transactions with historical data while complying with millisecond-level latencies. In this work, we do not require those expensive features by using recurrent neural networks and treating payments as an interleaved sequence, where the history of each card is an unbounded, irregular sub-sequence. We present a complete RNN framework to detect fraud in real-time, proposing an efficient ML pipeline from preprocessing to deployment. We show that these feature-free, multi-sequence RNNs outperform state-of-the-art models saving millions of dollars in fraud detection and using fewer computational resources.
