迈向基础购买模型：在交易序列上的预训练生成式自回归
Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
AI总结：
本文提出一种用于金融交易序列的生成式预训练方法，获取上下文化嵌入，并在包含51亿笔交易的大规模数据上预训练模型，显著提升了高精度阈值下的欺诈检测率与域外迁移能力。
摘要：
机器学习模型支撑着许多现代金融系统，用于欺诈检测和流失预测等用例。大多数模型基于带有手工设计特征的监督学习，这严重依赖于标记数据的可用性。大型自监督生成模型在自然语言处理和计算机视觉领域取得了巨大成功，但迄今为止尚未适应金融交易的多变量时间序列。在本文中，我们提出了一种生成式预训练方法，可用于获取金融交易的上下文化嵌入。在公共数据集上的基准测试表明，它在一系列下游任务上优于最先进的自监督方法。此外，我们使用包含来自180家发卡行的51亿笔交易的数据语料库，对嵌入模型进行了大规模预训练，并将其应用于留存数据集上的信用卡欺诈检测问题。该嵌入模型在高精度阈值下显著提高了价值检测率，并能很好地迁移到域外分布。
英文摘要：
Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineered features, which relies heavily on the availability of labelled data. Large self-supervised generative models have shown tremendous success in natural language processing and computer vision, yet so far they haven't been adapted to multivariate time series of financial transactions. In this paper, we present a generative pretraining method that can be used to obtain contextualised embeddings of financial transactions. Benchmarks on public datasets demonstrate that it outperforms state-of-the-art self-supervised methods on a range of downstream tasks. We additionally perform large-scale pretraining of an embedding model using a corpus of data from 180 issuing banks containing 5.1 billion transactions and apply it to the card fraud detection problem on hold-out datasets. The embedding model significantly improves value detection rate at high precision thresholds and transfers well to out-of-domain distributions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
