Using Machine Learning to Detect Fraudulent SMSs in Chichewa

TL;DR

This study introduces the first dataset for SMS fraud detection in Chichewa, demonstrating that machine learning models can achieve high accuracy, but performance drops with translation, emphasizing the need for language-specific models.

Contribution

The paper creates and evaluates the first Chichewa SMS fraud dataset and assesses the feasibility of machine learning models for this language, highlighting challenges in multilingual NLP.

Findings

Models achieved over 96% accuracy on Chichewa data

Performance declined when using translated datasets

Data preprocessing impacts model effectiveness in multilingual settings

Abstract

SMS enabled fraud is of great concern globally. Building classifiers based on machine learning for SMS fraud requires the use of suitable datasets for model training and validation. Most research has centred on the use of datasets of SMSs in English. This paper introduces a first dataset for SMS fraud detection in Chichewa, a major language in Africa, and reports on experiments with machine learning algorithms for classifying SMSs in Chichewa as fraud or non-fraud. We answer the broader research question of how feasible it is to develop machine learning classification models for Chichewa SMSs. To do that, we created three datasets. A small dataset of SMS in Chichewa was collected through primary research from a segment of the young population. We applied a label-preserving text transformations to increase its size. The enlarged dataset was translated into English using two approaches: human translation and machine translation. The Chichewa and the translated datasets were subjected to machine classification using random forest and logistic regression. Our findings indicate that both models achieved a promising accuracy of over 96% on the Chichewa dataset. There was a drop in performance when moving from the Chichewa to the translated dataset. This highlights the importance of data preprocessing, especially in multilingual or cross-lingual NLP tasks, and shows the challenges of relying on machine-translated text for training machine learning models. Our results underscore the importance of developing language specific models for SMS fraud detection to optimise accuracy and performance. Since most machine learning models require data preprocessing, it is essential to investigate the impact of the reliance on English-specific tools for data preprocessing.