Accessing the Untapped Potential of Large Language Models in Banking: A Capability Readiness Framework

Philipp Winder
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Philipp Winder: University of St.Gallen

No zqsa3_v1, OSF Preprints from Center for Open Science

Abstract: This paper presents a novel capability-based framework for assessing organizational readiness in deploying large language models (LLMs) in the banking sector. While LLMs offer significant potential across domains such as customer service, compliance, and risk assessment, banks face unique deployment challenges due to regulatory constraints, legacy systems, and data sensitivity. Building on the dynamic capability view and adapting maturity levels from the Capability Maturity Model Integration (CMMI), the framework identifies and structures the organizational, contextual, and technical capabilities necessary for effective LLM deployment. It introduces a maturity-scaled self-assessment tool that enables banks to evaluate their current LLM readiness, diagnose capability gaps, and guide strategic investment decisions. Although developed for banking, the framework offers conceptual relevance to other high-stakes, highly regulated sectors.

Date: 2025-06-17
New Economics Papers: this item is included in nep-ain
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:zqsa3_v1

DOI: 10.31219/osf.io/zqsa3_v1

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