Efficient Short-Text Classification of Banking Transaction Descriptions Using Support Vector Machines and Specialized Lexica

Core Concepts

A novel system that combines Natural Language Processing techniques with Machine Learning algorithms to efficiently classify banking transaction descriptions for personal finance management.

Abstract

The authors describe a short-text classification system for banking transaction (BT) descriptions. The system uses Natural Language Processing (NLP) and Machine Learning (ML) techniques, specifically a Support Vector Machine (SVM) classifier.

Key highlights:

• The system tackles the challenges of short-text classification, such as sparsity, real-time generation, and irregularity of vocabulary, by leveraging the particularities of the banking domain.
• It extracts linguistic knowledge in the form of specialized lexica for each BT category, which are crucial features for the classifier.
• The system also incorporates meta-information features such as transaction amount, date, and sign.
• To reduce the training set size, the authors propose a short text similarity detector based on the Jaccard distance.
• Experimental results on a real-world dataset of 30,844 BT descriptions show that the proposed system outperforms state-of-the-art approaches in terms of precision, while maintaining competitive recall and F-measure. The system is also more efficient in terms of training time.
• The authors present a real-world use case of their system in the CoinScrap personal finance management application.

Stats

The dataset comprises 30,844 BT descriptions from customer accounts of major Spanish banks, written mostly in Spanish and issued between August 2017 and February 2018.
The dataset has 15 category labels, with the number of instances per category ranging from 67 to 11,061.

Quotes

"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."
"Two key aspects are that words are seldom repeated in a given BT description and that few words are irrelevant. The level of significance of a word cannot be simply determined by its repetition within the text. However, for the same reasons, short texts are less noisy than long texts."

Key Insights Distilled From

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

by Silv... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08664.pdf

Deeper Inquiries

How can the proposed system be extended to handle multilingual BT descriptions?

The proposed system can be extended to handle multilingual BT descriptions by incorporating language detection techniques at the preprocessing stage. This would involve identifying the language of each BT description and then applying language-specific processing techniques, such as tokenization, stopword removal, and proper name detection. Additionally, the system could utilize language-specific lexica for feature extraction and classification. By training the system on multilingual datasets and incorporating language-specific features, the system can effectively classify BT descriptions in multiple languages.

What are the potential limitations of the Jaccard distance-based similarity detector, and how could it be improved?

One potential limitation of the Jaccard distance-based similarity detector is its sensitivity to variations in text length and vocabulary. Since it relies on the overlap of words between texts, longer texts may have a higher chance of similarity even if the content is different. Additionally, the detector may struggle with texts that contain synonyms or paraphrases, as it only considers exact word matches.
To improve the Jaccard distance-based similarity detector, one approach could be to incorporate techniques for synonym detection and text normalization. By expanding the detector to consider variations of words and phrases, such as stemming and lemmatization, it can capture semantic similarities more effectively. Additionally, utilizing more advanced similarity metrics that account for word semantics, such as cosine similarity or word embeddings, could enhance the detector's accuracy and robustness.

How could the system's performance be further enhanced by incorporating external knowledge sources, such as financial ontologies or domain-specific word embeddings?

Incorporating external knowledge sources, such as financial ontologies or domain-specific word embeddings, can significantly enhance the system's performance in classifying BT descriptions. By leveraging financial ontologies, the system can gain a deeper understanding of financial terms, relationships, and concepts, improving the accuracy of classification. Domain-specific word embeddings can capture the semantic relationships between words in the context of banking transactions, providing richer representations for the classification model.
To integrate external knowledge sources effectively, the system can preprocess the BT descriptions to align them with the ontology or word embeddings. This alignment can help extract relevant features and capture the nuances of financial language. Additionally, the system can use the ontology or word embeddings to expand the lexica and feature set, providing more contextually relevant information for classification. By incorporating external knowledge sources, the system can improve its classification accuracy, especially in handling complex and specialized language in banking transactions.