ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Finance
FinFakeBERT: Financial Fake News Detection
Provisionally accepted
- Zurich University of Applied Sciences, Winterthur, Switzerland
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The intentional use of fake news for financial manipulation or the disruption of financial markets is a serious concern, especially with the rise of generative artificial intelligence, which will significantly increase its dissemination. A lack of open-access, labeled financial fake news data poses challenges when training effective models for financial fake news detection. To address these challenges, we present FinFakeBERT, a family of models trained using newly curated fake news data. We show that fine-tuning BERT with a small set of actual financial fake news after fine-tuning with a large cross-domain fake news dataset and accurate financial news articles leads to a high fake news detection accuracy and significantly reduces the false positive rate (FPR) when tested on several large sets of real financial news articles. Our best model achieves a 2.1% false positive rate (FPR) on real financial news, whereas available benchmark fake-news detectors exhibit FPRs that are more than three-to tenfold higher.
Keywords: Fake news detection, Financial Fake News, Domain shift, Large LanguageModels, machine learning
Received: 01 Apr 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Fazlija, Bakiji and Dauti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Bledar Fazlija, bledar.fazlija@zhaw.ch
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.