 Beyond the black box: operationalising explicability in artificial intelligence for financial institutionsSam Solaimani and Phoebe Long International Journal of Business Information Systems, 2025, vol. 49, issue 5, 1-38 Abstract: Artificial intelligence (AI) is transforming the finance sector, driving advancements in fraud detection, risk profiling, and trading strategies. Despite its potential, AI requires robust governance to prevent perpetuating unconscious biases, achievable through the principle of explicability. This study examines explicability in ethical AI governance within finance, focusing on its conceptualisation and operationalisation. Drawing on interdisciplinary literature, the study conceptualises an integrative maturity framework around three core dimensions: transparency, interpretability, and accountability. The framework provides actionable guidance for operationalisation through progressive procedures, tools, and interventions. Empirical validation through expert interviews reveals that explicability should be addressed holistically, operationalised incrementally, and implemented consistently. The proposed explicability maturity framework supports firms in ethically and effectively adopting AI, advancing both academic discourse and industry practices. Keywords: ethics; explicability; operationalisation; artificial intelligence; financial institutions; maturity model. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:49:y:2025:i:5:p:1-38 Access Statistics for this article More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd |
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