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<p>The rapid adoption of artificial intelligence (AI) in the financial sector has intensified concerns regarding responsible use, governance, and long-term capability development. While prior studies examine AI adoption, regulation, ethical principles, or performance outcomes, limited empirical research explains how banking and regulated financial service organizations systematically build responsible AI capabilities as part of their strategic management processes. In particular, existing studies do not structurally model the interdependencies among AI management factors nor link them to dynamic organizational capabilities. Addressing this gap, this study presents an original empirical investigation of responsible AI management in commercial banks and regulated financial institutions engaged in risk management, compliance, credit assessment, and customer-facing financial services. The study makes an original theoretical contribution by integrating Fuzzy Interpretive Structural Modelling (FISM) with Dynamic Capabilities Theory (DCT) to develop a new, theory-informed capability-building framework. A three-phase mixed-methods design was employed. In the first phase, open-ended questionnaires and in-depth interviews with 26 domain experts, along with Nominal Group Technique (NGT) sessions involving 11 experts, identified 14 critical factors influencing responsible AI management. In the second phase, FISM was applied to model the hierarchical and contextual interrelationships among these factors. In the final phase, follow-up interviews mapped these factors to the sensing, seizing, and transforming dimensions of DCT. The findings generate new empirical insights demonstrating that responsible AI in banking functions as a second-order dynamic capability extending beyond technological readiness. While financial institutions possess foundational AI resources, they lack specialized skills and governance structures required for responsible AI integration. Ethical governance mechanisms, workforce development, legacy system alignment, and technology partnerships emerge as key driving enablers. The study offers an original, empirically grounded framework that advances responsible AI scholarship by linking structural factor modelling with dynamic capability development, providing a structured roadmap for financial managers and policymakers seeking to align AI innovation with risk management and ethical accountability.</p>

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Building capabilities for responsible AI in finance: insights from Fuzzy ISM

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Jaspreet Kaur,
  • Hans Kaushik,
  • Sanjeev Swami,
  • Sumita Srivastava,
  • Bharti Kumari

摘要

The rapid adoption of artificial intelligence (AI) in the financial sector has intensified concerns regarding responsible use, governance, and long-term capability development. While prior studies examine AI adoption, regulation, ethical principles, or performance outcomes, limited empirical research explains how banking and regulated financial service organizations systematically build responsible AI capabilities as part of their strategic management processes. In particular, existing studies do not structurally model the interdependencies among AI management factors nor link them to dynamic organizational capabilities. Addressing this gap, this study presents an original empirical investigation of responsible AI management in commercial banks and regulated financial institutions engaged in risk management, compliance, credit assessment, and customer-facing financial services. The study makes an original theoretical contribution by integrating Fuzzy Interpretive Structural Modelling (FISM) with Dynamic Capabilities Theory (DCT) to develop a new, theory-informed capability-building framework. A three-phase mixed-methods design was employed. In the first phase, open-ended questionnaires and in-depth interviews with 26 domain experts, along with Nominal Group Technique (NGT) sessions involving 11 experts, identified 14 critical factors influencing responsible AI management. In the second phase, FISM was applied to model the hierarchical and contextual interrelationships among these factors. In the final phase, follow-up interviews mapped these factors to the sensing, seizing, and transforming dimensions of DCT. The findings generate new empirical insights demonstrating that responsible AI in banking functions as a second-order dynamic capability extending beyond technological readiness. While financial institutions possess foundational AI resources, they lack specialized skills and governance structures required for responsible AI integration. Ethical governance mechanisms, workforce development, legacy system alignment, and technology partnerships emerge as key driving enablers. The study offers an original, empirically grounded framework that advances responsible AI scholarship by linking structural factor modelling with dynamic capability development, providing a structured roadmap for financial managers and policymakers seeking to align AI innovation with risk management and ethical accountability.