TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding
Chin-Chia Michael Yeh, Uday Singh Saini, Xin Dai, Xiran Fan, Shubham Jain, Yujie Fan, Jiarui Sun, Junpeng Wang, Menghai Pan, Yingtong Dou, Yuzhong Chen, Vineeth Rakesh, Liang Wang, Yan Zheng, Mahashweta Das

TL;DR
TREASURE is a transformer-based foundation model designed for transaction data, improving behavior detection and recommendations in payment networks through comprehensive modeling of consumer and network signals.
Contribution
The paper introduces TREASURE, a scalable transformer model tailored for transaction data, with novel input modules and training paradigms for high-cardinality categorical attributes.
Findings
Increases abnormal behavior detection by 111% over existing systems.
Enhances recommendation models by 104% using learned embeddings.
Demonstrates effectiveness through ablation studies and industry benchmarks.
Abstract
Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling this data can enable applications such as abnormal behavior detection and consumer-level insights for hyper-personalized experiences, ultimately improving people's lives. In this paper, we present TREASURE, TRansformer Engine As Scalable Universal transaction Representation Encoder, a multipurpose transformer-based foundation model specifically designed for transaction data. The model simultaneously captures both consumer behavior and payment network signals (such as response codes and system flags), providing comprehensive information necessary for applications like accurate recommendation systems and abnormal behavior detection. Verified with industry-grade datasets, TREASURE features three key capabilities: 1) an input module with dedicated…
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