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Artificial Intelligence as a Catalyst for Change in Accounting and Finance: Exploring Applications, Opportunities and Challenges (Iconic Research and Engineering Journals)

Research paperIconic Research and Engineering JournalsNovember 202614 pages21 min readRekha Susan LeoOriginal source
Key number
1,600 professionals
Finance Professionals Surveyed p.7
Key number
256 articles
Accounting and Finance AI Articles Synthesised p.2

Summary

  • Sanz Martin et al. (2026) synthesised 256 articles to highlight transparency, ethics, security, and skills in AI accounting research.p.2
  • Abbas (2026) systematically reviewed 91 studies on management accounting, concluding that AI alters information flows, roles, and skill requirements.p.2
  • Singh et al. (2025) reviewed 157 studies on AI in banking and finance, identifying recurring focus on customer service, regulation, and explainability.p.2
  • The Reserve Bank of India reported implementing an AI/ML model named MuleHunter.AI to detect potential mule bank accounts.p.6
  • A global survey by ACCA and CA ANZ of 1,600 finance professionals showed that data quality, skills, and integration are major adoption barriers.p.7
  • The paper identifies 5 key capability groups necessary for professionals operating in AI-enabled accounting and finance environments.p.8

Key insights

  1. AI shifts accounting technology from back-office automation toward analytical augmentation, continuous monitoring, and strategic decision support.p.1
  2. Practical adoption in auditing remains more advanced for relatively simple AI tools than for complex models.p.5
  3. Generative AI can produce fluent commentary, but language fluency does not establish factual correctness or standard compliance in reporting.p.5
  4. Inputting sensitive accounting records into uncontrolled external AI platforms exposes organizations to substantial confidentiality risks.p.7
  5. Poorly designed automated rules create systematic risk by rapidly repeating errors across thousands of transactions.p.7
  6. Excessive reliance on automated outputs can cause deskilling and erode critical professional scepticism over time.p.8
  7. Governance serves as an essential component of value creation rather than a post-implementation compliance constraint.p.10
  8. Organizational value is maximized when AI handles repetitive processing while human professionals retain judgement, ethics, and accountability.p.12

Topics and entities

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