Specialized text classification: an approach to classifying Open Banking transactions

ArXiv ID: 2504.12319 “View on arXiv”

Authors: Unknown

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.

Keywords: Open Banking, Transaction Classification, Natural Language Processing (NLP), PSD2, Risk Management

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper’s mathematics is primarily applied machine learning (text preprocessing, vectorization, classifiers) without advanced theoretical derivations, keeping complexity moderate. It demonstrates strong empirical rigor through a concrete system design with data processing steps, specific dataset description, and performance evaluation on real banking data, making it backtest-ready.

  flowchart TD
    A["Research Goal<br>Specialized French Open Banking<br>Transaction Classification"] --> B["Data Collection<br>French Banking Transactions"]
    B --> C["Preprocessing & Labeling<br>Domain-specific cleaning &<br>Manual annotation"]
    C --> D["Modeling<br>Custom NLP vs. Generic Models"]
    D --> E["Evaluation<br>Performance Metrics<br>(Accuracy, F1-Score)"]
    E --> F["Key Findings<br>Custom system outperforms<br>generic approaches in<br>French banking context"]