With the explosive growth of e-commerce, online transaction fraud has become
one of the biggest challenges for e-commerce platforms. The historical
behaviors of users provide rich information for digging into the users' fraud
risk. While considerable efforts have been made in this direction, a
long-standing challenge is how to effectively exploit internal user information
and provide explainable prediction results. In fact, the value variations of
same field from different events and the interactions of different fields
inside one event have proven to be strong indicators for fraudulent behaviors.
In this paper, we propose the Dual Importance-aware Factorization Machines
(DIFM), which exploits the internal field information among users' behavior
sequence from dual perspectives, i.e., field value variations and field
interactions simultaneously for fraud detection. The proposed model is deployed
in the risk management system of one of the world's largest e-commerce
platforms, which utilize it to provide real-time transaction fraud detection.
Experimental results on real industrial data from different regions in the
platform clearly demonstrate that our model achieves significant improvements
compared with various state-of-the-art baseline models. Moreover, the DIFM
could also give an insight into the explanation of the prediction results from
dual perspectives.

本文提出利用用户行为序列中的内部字段信息，从值变化和字段交互的双重视角利用 Dual Importance-aware Factorization Machines （DIFM）在实时交易欺诈检测方面实现显著的改进。

同时建模字段值变化和字段交互以进行欺诈检测

Modeling the Field Value Variations and Field Interactions  Simultaneously for Fraud Detection

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.

使用循环神经网络作为实时欺诈检测系统，以短计算时间和少量能源资源实现了比现有技术更准确的多序列特征自由欺诈检测，节约了数百万美元。