TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917390784135168 |
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| author | Yeh, Chin-Chia Michael Saini, Uday Singh Dai, Xin Fan, Xiran Jain, Shubham Fan, Yujie Sun, Jiarui Wang, Junpeng Pan, Menghai Dou, Yingtong Chen, Yuzhong Rakesh, Vineeth Wang, Liang Zheng, Yan Das, Mahashweta |
| author_facet | Yeh, Chin-Chia Michael Saini, Uday Singh Dai, Xin Fan, Xiran Jain, Shubham Fan, Yujie Sun, Jiarui Wang, Junpeng Pan, Menghai Dou, Yingtong Chen, Yuzhong Rakesh, Vineeth Wang, Liang Zheng, Yan Das, Mahashweta |
| contents | 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 sub-modules for static and dynamic attributes, enabling more efficient training and inference; 2) an efficient and effective training paradigm for predicting high-cardinality categorical attributes; and 3) demonstrated effectiveness as both a standalone model that increases abnormal behavior detection performance by 111% over production systems and an embedding provider that enhances recommendation models by 104%. We present key insights from extensive ablation studies, benchmarks against production models, and case studies, highlighting valuable knowledge gained from developing TREASURE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding Yeh, Chin-Chia Michael Saini, Uday Singh Dai, Xin Fan, Xiran Jain, Shubham Fan, Yujie Sun, Jiarui Wang, Junpeng Pan, Menghai Dou, Yingtong Chen, Yuzhong Rakesh, Vineeth Wang, Liang Zheng, Yan Das, Mahashweta Machine Learning Artificial Intelligence 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 sub-modules for static and dynamic attributes, enabling more efficient training and inference; 2) an efficient and effective training paradigm for predicting high-cardinality categorical attributes; and 3) demonstrated effectiveness as both a standalone model that increases abnormal behavior detection performance by 111% over production systems and an embedding provider that enhances recommendation models by 104%. We present key insights from extensive ablation studies, benchmarks against production models, and case studies, highlighting valuable knowledge gained from developing TREASURE. |
| title | TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.19693 |