Financial decision-making is heavily influenced by persuasion techniques employed by financial advisors, marketers, and companies. Researchers require robust datasets for model development and evaluation to understand and predict this influence. This paper introduces FINPS1, a novel, binary-classified dataset identifying persuasive language (e.g., authority, social proof, scarcity, reciprocity) in the context of financial decisions. Furthermore, Our study demonstrates and evaluates a state-of-the-art method to fine-tune a small language model for binary classification of persuasive content in financial communications. This model aims to empower financial analysts, marketers, and regulators to identify and assess the use of persuasion in financial advice, advertisements, and communication channels. The model’s effectiveness is measured by accuracy, precision, recall, and F1 score, with the goal of achieving high-performance metrics for discerning persuasive language in financial contexts.