Navigating the Decision-making Landscape of AI in Risk Finance: Techno-Accountability Unveiled

Helmi Issa, Roy Dakroub, Hussein Lakkis and Jad Jaber
Additional contact information
Helmi Issa: BSB - Burgundy School of Business (BSB) - ESC Dijon-Bourgogne, CEREN - Centre de Recherche sur les Entreprises - BSB - Burgundy School of Business (BSB) - ESC Dijon-Bourgogne

Post-Print from HAL

Abstract: The integration of AI systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift towards data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contributes to the risk analysis domain by improving the Failure Mode and Effect Analysis (FMEA) framework to better fit the rapidly evolving landscape of AI-driven innovations.

Keywords: AI; Techno-stressors; Techno-accountability; Risk analysis (search for similar items in EconPapers)
Date: 2025-04-01
References: Add references at CitEc
Citations:

Published in Risk Analysis, 2025, 45 (4), pp.808-829. ⟨10.1111/risa.14336⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05799088

DOI: 10.1111/risa.14336

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().