Abstract
With the introduction of the PSD2 regulation in the EU which established the Open Banking framework, a new window of opportunities has opened for banks and fintechs to explore and enrich Bank transaction descriptions with the aim of building a better understanding of customer behavior, while using this understanding to prevent fraud, reduce risks and offer more competitive and tailored services. And although the usage of natural language processing models and techniques has seen an incredible progress in various applications and domains over the past few years, custom applications based on domain-specific text corpus remain unaddressed especially in the banking sector. In this paper, we introduce a language-based Open Banking transaction classification system with a focus on the french market and french language text. The system encompasses data collection, labeling, preprocessing, modeling, and evaluation stages. Unlike previous studies that focus on general classification approaches, this system is specifically tailored to address the challenges posed by training a language model with a specialized text corpus (Banking data in the French context). By incorporating language-specific techniques and domain knowledge, the proposed system demonstrates enhanced performance and efficiency compared to generic approaches.
Abstract (translated)
随着欧盟引入PSD2法规，建立了开放银行框架，为银行和金融科技公司提供了一个新的机遇窗口，可以探索并丰富银行交易描述，以更好地理解客户行为。利用这种理解来预防欺诈、降低风险，并提供更具竞争力且个性化的服务。尽管近年来自然语言处理模型和技术在各种应用和领域中取得了惊人的进展，但在特定领域的文本语料库基础上开发定制应用程序方面仍然未得到充分解决，尤其是在银行业。 本文介绍了一种基于语言的开放银行交易分类系统，重点关注法国市场和法语文本。该系统涵盖了数据收集、标注、预处理、建模和评估阶段。与以往专注于通用分类方法的研究不同，本系统特别针对使用特定领域文本语料库（在法国背景下为银行业务数据）训练语言模型所面临的挑战进行了定制化设计。通过结合语言特有技术和领域知识，该提议的系统相比通用方法表现出更优的性能和效率。 这一系统的开发标志着向更精细、更具针对性的数据驱动解决方案迈出了重要一步，这将有助于银行更好地理解客户需求，并提供更加个性化且有效的服务。
URL
https://arxiv.org/abs/2504.12319
