46 citations · 90 across the 10 of their papers we have counts for
10 papers
LLM-Enhanced Self-Evolving Reinforcement Learning for Multi-Step E-Commerce Payment Fraud Risk Detection
Bo Qu, Zhurong Wang, Daisuke Yagi +4
This paper presents a novel approach to e-commerce payment fraud detection by integrating reinforcement learning (RL) with Large Language Models (LLMs). By framing transaction risk…
MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage
Yongjun He, Roger Waleffe, Zhichao Han +8
Many modern machine learning (ML) methods rely on embedding models to learn vector representations (embeddings) for a set of entities (embedding tables). As increasingly diverse ML…
Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection
Bo Qu, Zhurong Wang, Minghao Gu +4
The burgeoning e-Commerce sector requires advanced solutions for the detection of transaction fraud. With an increasing risk of financial information theft and account takeovers, d…
Behavioral graph fraud detection in E-commerce
Hang Yin, Zitao Zhang, Zhurong Wang +5
In e-commerce industry, graph neural network methods are the new trends for transaction risk modeling.The power of graph algorithms lie in the capability to catch transaction linki…
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
Kenza Amara, Rex Ying, Zitao Zhang +5
As one of the most popular machine learning models today, graph neural networks (GNNs) have attracted intense interest recently, and so does their explainability. Users are increas…
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Mingxuan Lu, Zhichao Han, Susie Xi Rao +6
Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed…
Modelling graph dynamics in fraud detection with "Attention"
Susie Xi Rao, Clémence Lanfranchi, Shuai Zhang +7
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite th…
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
Mingxuan Lu, Zhichao Han, Zitao Zhang +2
Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces. In order to leverage graph networks to decrease fraud rate efficiently and…
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks
Susie Xi Rao, Shuai Zhang, Zhichao Han +6
Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame. To monitor th…
xFraud: Explainable Fraud Transaction Detection
Susie Xi Rao, Shuai Zhang, Zhichao Han +6
At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud,…