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AI Failures and Financial Judgment: What Finance Professionals Must Learn

Kapila Dodamgoda

Management Accounting Frontiers2026https://doi.org/10.52153/oaj0135124article
ABDC C
Weight
0.50

What the paper says

Artificial intelligence is now embedded across finance, accounting, and banking, influencing forecasts, valuations, credit decisions, risk management, and strategic planning. While AI-driven systems offer efficiency and analytical scale, recent failures highlight that these tools also introduce new forms of professional risk. Drawing on real-world cases across recruitment, forecasting, trading, credit assessment, and enterprise decision support, this article examines where and why AI systems fail in financial contexts. The analysis shows that AI failures are rarely caused by technology alone, but by biased data, opaque models, weak governance, and over-reliance on automated outputs. For management accountants and finance professionals, these failures reinforce the enduring importance of professional judgement, scepticism, and accountability. The article argues that AI should be treated as a decision-support tool, subject to the same rigour, controls, and ethical standards applied to traditional financial models, thereby reaffirming the central role of the finance profession in safeguarding decision quality and trust. A number of lessons from real-world ai failures in finance, accounting, and banking are presented.

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https://doi.org/https://doi.org/10.52153/oaj0135124

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@article{kapila2026,
  title        = {{AI Failures and Financial Judgment: What Finance Professionals Must Learn}},
  author       = {Kapila Dodamgoda},
  journal      = {Management Accounting Frontiers},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.52153/oaj0135124},
}

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AI Failures and Financial Judgment: What Finance Professionals Must Learn

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.