Omega^2 is a newly introduced framework designed for corporate credit scoring that utilizes a Large Language Model to improve both predictive reliability and interpretability. The model was evaluated on a dataset comprising 7,800 corporate credit ratings sourced from major rating agencies. By integrating structured financial data with advanced machine learning techniques, Omega^2 aims to enhance the accuracy and transparency of credit risk assessments. The claims regarding its improved predictive reliability and interpretability are positively stated in the research. This approach reflects a growing trend in applying sophisticated AI models to financial applications, combining traditional data with language-based models to better capture complex patterns. The development of Omega^2 contributes to ongoing efforts to refine credit scoring methodologies using cutting-edge technology.