Assessing the Capabilities and Limitations of FinGPT Model in Financial NLP Applications

The financial industry has long been a pioneer in adopting cutting-edge technologies to enhance operational efficiency, accuracy, and strategic decision-making [2]. With the exponential growth of structured and unstructured data, particularly from news feeds, earnings reports, disclosures, and social media, there is an increasing demand for intelligent systems capable of processing human language at scale [11]. Initially, the industry relied on rule-based approaches and traditional statistical techniques such as bag-of-words and TF-IDF [28], which offered limited semantic understanding. As noted by Abubakar et al.[1], these limitations triggered a shift toward machine learning and deep learning models that, while better at capturing patterns, still required substantial domain-specific feature engineering. This landscape was significantly transformed with the introduction of transformer-based architectures, most notably the Generative Pre-trained Transformer (GPT) family [5]. These models demonstrated the power of large-scale pretraining followed by task-specific fine-tuning, enabling generalization across diverse NLP tasks. Models such as GPT-3, GPT-4, BERT, and T5 have delivered state-of-the-art results in sentiment analysis, summarization, question answering, and named entity recognition [13]. Beyond LLMs, the broader field of Generative AI (GAI)--including GANs, V AEs, and diffusion models--has found increasing relevance in finance, facilitating applications such as synthetic data generation, automated reporting, and scenario simulation [32, 31]. LLMs have emerged as essential tools in processing unstructured financial text, especially models fine-tuned on finance-specific corpora like FinBERT, BloombergGPT, and FinGPT [4, 39].