# Multilingual Financial Fraud Detection Using Machine Learning and Transformer Models: A Bangla-English Study

arXiv: https://arxiv.org/abs/2603.11358 · HTML: https://mlsanity.com/paper/2603.11358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 15 of 15)

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- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](https://mlsanity.com/paper/2005.04322/) (2020)
- [SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark](https://mlsanity.com/paper/1709.08920/) (2017)
- [A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models](https://mlsanity.com/paper/2107.03844/) (2021)
- [Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis](https://mlsanity.com/paper/2509.08007/) (2025)
- [Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness](https://mlsanity.com/paper/2510.16171/) (2025)
- [Detecting Conspiracy Theory Against COVID-19 Vaccines](https://mlsanity.com/paper/2211.13003/) (2022)
- [Learning to Select Like Humans: Explainable Active Learning for Medical Imaging](https://mlsanity.com/paper/2602.13308/) (2026)

## Abstract (arXiv)

Financial fraud detection has emerged as a critical research challenge amid the rapid expansion of digital financial platforms. Although machine learning approaches have demonstrated strong performance in identifying fraudulent activities, most existing research focuses exclusively on English-language data, limiting applicability to multilingual contexts. Bangla (Bengali), despite being spoken by over 250 million people, remains largely unexplored in this domain. In this work, we investigate financial fraud detection in a multilingual Bangla-English setting using a dataset comprising legitimate and fraudulent financial messages. We evaluate classical machine learning models (Logistic Regression, Linear SVM, and Ensemble classifiers) using TF-IDF features alongside transformer-based architectures. Experimental results using 5-fold stratified cross-validation demonstrate that Linear SVM achieves the best performance with 91.59 percent accuracy and 91.30 percent F1 score, outperforming the transformer model (89.49 percent accuracy, 88.88 percent F1) by approximately 2 percentage points. The transformer exhibits higher fraud recall (94.19 percent) but suffers from elevated false positive rates. Exploratory analysis reveals distinctive patterns: scam messages are longer, contain urgency-inducing terms, and frequently include URLs (32 percent) and phone numbers (97 percent), while legitimate messages feature transactional confirmations and specific currency references. Our findings highlight that classical machine learning with well-crafted features remains competitive for multilingual fraud detection, while also underscoring the challenges posed by linguistic diversity, code-mixing, and low-resource language constraints.