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Beyond the black box: operationalising explicability in artificial intelligence for financial institutions

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  • Sam Solaimani
  • Phoebe Long

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.

Suggested Citation

  • Sam Solaimani & Phoebe Long, 2025. "Beyond the black box: operationalising explicability in artificial intelligence for financial institutions," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 49(5), pages 1-38.
    • Handle: RePEc:ids:ijbisy:v:49:y:2025:i:5:p:1-38
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