Quantifying Generative AI’s Superiority in Peer Group Identification : Empirical Validation for Strategic Transformation in Fintech

Abstract

This thesis empirically evaluates the transformative potential of Generative Artificial Intelligence (GenAI) in revolutionizing peer group identification in finance. Traditional methodologies, reliant on static industrial classification, suffer from structural inadequacy and high analytical error. This study employs a GenAI-powered solution and demonstrates its statistically significant superiority over traditional methods, achieving the lowest absolute cumulative Z-scores; our measure of financial homogeneity. The findings confirm that the GenAI framework successfully identifies complex, non-traditional competitive peers where traditional systems fail. Utilizing the transformative strategies concept, the research analyzes the strategic implications, showing that GenAI enables organizations to resolve core strategic tensions. The thesis concludes that GenAI establishes a new, accurate operational standard, driving strategic transformation within the Fintech sector.