AI in Operational Strategy: GPT & Perplexity Models in BofA's 2025 Branch Closure Case Study

Abstract

The dynamic landscape of banking operations increasingly benefits from artificial intelligence, enhancing decision-making capabilities. In this study, we implemented two models: one under GPT framework and another using Perplexity, to leverage their powerful computational abilities to analyze and interpret financial data and operational trends. We employed sophisticated attention mechanisms and multi-head attention within these Large Language Models (LLMs) to investigate the strategic reasons behind the anticipated 2025 closures of Bank of America branches [12]. Using unstructured data, this study adopts a mixed-method framework that combines mathematical theories with advanced prompt engineering to compare the effectiveness of ChatGPT and Perplexity AI. By applying this approach, the study aims to highlight how each model performs in analyzing complex, real-world information and uncovering meaningful insights related to branch closures. The comparative analysis confirms that Perplexity AI outperformed ChatGPT by providing more precise and contextual relevant insights that are crucial for strategic decision-making in the banking sector. These results highlight the advanced capabilities of LLMs equipped with attention technologies in processing complex, multivariable datasets effectively. The implications of our research extend beyond just operational strategies within banks. They offer substantial insights that could revolutionize customer service management and investment strategies, contributing to a more nuanced understanding of evolving market dynamics. The study underscores the transformative potential of advanced LLMs in banking, recommending their broader adoption to effectively navigate and adapt to the rapid digital transformations in the industry.

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