{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T01:53:28Z","timestamp":1772848408991,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This study is an in-depth exploration of the nascent field of Natural Language Processing (NLP) and generative Artificial Intelligence (AI), and it concentrates on the vital task of distinguishing between human-generated text and content that has been produced by AI models. Particularly, this research pioneers the identification of financial text derived from AI models such as ChatGPT and paraphrasing tools like QuillBot. While our primary focus is on financial content, we have also pinpointed texts generated by paragraph rewriting tools and utilized ChatGPT for various contexts this multiclass identification was missing in previous studies. In this paper, we use a comprehensive feature extraction methodology that combines TF\u2013IDF with Word2Vec, along with individual feature extraction methods. Importantly, combining a Random Forest model with Word2Vec results in impressive outcomes. Moreover, this study investigates the significance of the window size parameters in the Word2Vec approach, revealing that a window size of one produces outstanding scores across various metrics, including accuracy, precision, recall and the F1 measure, all reaching a notable value of 0.74. In addition to this, our developed model performs well in classification, attaining AUC values of 0.94 for the \u2018GPT\u2019 class; 0.77 for the \u2018Quil\u2019 class; and 0.89 for the \u2018Real\u2019 class. We also achieved an accuracy of 0.72, precision of 0.71, recall of 0.72, and F1 of 0.71 for our extended prepared dataset. This study contributes significantly to the evolving landscape of AI text identification, providing valuable insights and promising directions for future research.<\/jats:p>","DOI":"10.3390\/computation12050101","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T06:14:42Z","timestamp":1715753682000},"page":"101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Unveiling AI-Generated Financial Text: A Computational Approach Using Natural Language Processing and Generative Artificial Intelligence"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5583-1253","authenticated-orcid":false,"given":"Muhammad Asad","family":"Arshed","sequence":"first","affiliation":[{"name":"Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2911-6480","authenticated-orcid":false,"given":"\u0218tefan Cristian","family":"Gherghina","sequence":"additional","affiliation":[{"name":"Department of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1284-234X","authenticated-orcid":false,"given":"Christine","family":"Dewi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia"},{"name":"School of Information Technology, Deakin University, Campus 221 Burwood Hwy, Burwood, VIC 3125, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asma","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2606-2405","authenticated-orcid":false,"given":"Shahzad","family":"Mumtaz","sequence":"additional","affiliation":[{"name":"Department of Data Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan"},{"name":"School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3FX, Scotland, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Muneer, A., Alwadain, A., Ragab, M.G., and Alqushaibi, A. 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