# Identifying Banking Transaction Descriptions via Support Vector Machine Short-Text Classification Based on a Specialized Labelled Corpus

arXiv: https://arxiv.org/abs/2404.08664 · HTML: https://mlsanity.com/paper/2404.08664/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 84 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services](https://mlsanity.com/paper/1309.3945/) (2013)
- [ALL-IN-1: Short Text Classification with One Model for All Languages](https://mlsanity.com/paper/1710.09589/) (2017)
- [A Data Mining Approach to Predict Prospective Business Sectors for Lending in Retail Banking Using Decision Tree](https://mlsanity.com/paper/1504.02018/) (2015)

## Built on by (most cited first, 4 of 4)

- [Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing](https://mlsanity.com/paper/2405.14505/) (2024)
- [Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation](https://mlsanity.com/paper/2404.01338/) (2024)
- [Specialized text classification: an approach to classifying Open Banking transactions](https://mlsanity.com/paper/2504.12319/) (2025)
- [Where A Small Language Model Helps in Invoice Categorisation, Understood Through Embedding Geometry](https://mlsanity.com/paper/2608.18033/) (2026)

## Abstract (arXiv)

Short texts are omnipresent in real-time news, social network commentaries, etc. Traditional text representation methods have been successfully applied to self-contained documents of medium size. However, information in short texts is often insufficient, due, for example, to the use of mnemonics, which makes them hard to classify. Therefore, the particularities of specific domains must be exploited. In this article we describe a novel system that combines Natural Language Processing techniques with Machine Learning algorithms to classify banking transaction descriptions for personal finance management, a problem that was not previously considered in the literature. We trained and tested that system on a labelled dataset with real customer transactions that will be available to other researchers on request. Motivated by existing solutions in spam detection, we also propose a short text similarity detector to reduce training set size based on the Jaccard distance. Experimental results with a two-stage classifier combining this detector with a SVM indicate a high accuracy in comparison with alternative approaches, taking into account complexity and computing time. Finally, we present a use case with a personal finance application, CoinScrap, which is available at Google Play and App Store.