Submission declined on 13 August 2025 by Caleb Stanford (talk).
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| Submission declined on 9 August 2025 by Caleb Stanford (talk). Your draft shows signs of having been generated by a large language model, such as ChatGPT. Their outputs usually have multiple issues that prevent them from meeting our guidelines on writing articles. These include: Declined by Caleb Stanford 14 days ago.
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| Submission declined on 27 July 2025 by MCE89 (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Your draft shows signs of having been generated by a large language model, such as ChatGPT. Their outputs usually have multiple issues that prevent them from meeting our guidelines on writing articles. These include: Declined by MCE89 27 days ago.
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Comment: Topic is not notable. Article likely written in whole or in part by AI. Caleb Stanford (talk) 20:58, 13 August 2025 (UTC)
- Comment: In accordance with the Wikimedia Foundation's Terms of Use, I disclose that I have been paid by my employer for my contributions to this article. Herohcreatives (talk) 13:06, 15 July 2025 (UTC)
AI in financial close
editArtificial intelligence (AI) in financial close refers to the application of machine learning (ML), natural language processing (NLP), and generative AI to automate and improve tasks carried out at the end of a reporting period, including account reconciliation, journal entry preparation, and variance analysis. Adoption has grown quickly in recent years: a 2023 McKinsey survey found that one-third of organizations were already using generative AI in at least one business function, rising to 71 percent by mid-2024.[1] [2] In 2025, the World Economic Forum identified financial close and related processes as among the most automation-ready areas in financial services, estimating that 32–39 percent of tasks could be fully automated.[3]
Overview
editThe financial close process brings together an organization’s financial data to produce compliant reports. Typical activities include account reconciliation, journal entries, and preparation of financial statements.[4] When performed manually, often with spreadsheets and sequential approvals, the process can be slow and prone to error.[5] Since the late 2010s, artificial intelligence has been applied to reduce repetitive work and improve accuracy, with tools such as anomaly detection and predictive analytics.[6]
Technologies used
editSeveral artificial intelligence methods are applied in the financial close process:
- Machine learning (ML) is used to identify anomalies and automate journal entries by analyzing historical data. A 2024 study reported that a supervised ML algorithm reached a 93 percent detection rate for anomalies in payment systems.[7] An article in Accounting Insights (2024) described AI-based reconciliation tools that automate anomaly detection and validation in financial close.[8]
- Natural language processing (NLP) extracts information from unstructured documents such as invoices and contracts, reducing manual processing.[9] A Deloitte report from 2024 noted that NLP, within generative AI, is being used to streamline accrual scripting and data extraction.[10]
- Generative AI (GenAI) generates variance explanations and task lists. Deloitte reported in 2024 that GenAI improves workflow automation.[10] A Forbes Technology Council article in 2025 highlighted its role in automating reconciliation in fintech, noting reduced errors through anomaly detection.[11]
- Agent-based AI coordinates multi-step workflows such as exception routing. KPMG’s 2025 Intelligent Close initiative applied agentic AI to autonomous accounting.[12] A 2025 article from the Forbes Finance Council described its use in automating complex financial workflows, including autonomous closes.[13]
Comparison with traditional approaches
edit| Aspect | Traditional methods | AI-enabled methods | Source |
|---|---|---|---|
| Data reconciliation | Manual spreadsheet matching; prone to errors | Automated anomaly detection with real-time validation | [8][14] |
| Journal entries | Manual input with sequential approvals | Predictive suggestions and automated posting | [10] |
| Close cycle time | Typically 10–15 days | Six days or fewer under high automation | [15] |
Benefits
editThe use of artificial intelligence in financial close is associated with shorter cycle times and fewer errors. A 2024 report by Deloitte found that generative AI supports the close process by automating tasks and strengthening audit readiness through ___domain-specific knowledge bases.[10] KPMG’s 2025 Intelligent Close initiative reported lower human intervention and reduced risk of error through AI-driven anomaly detection.[12] In 2025, the World Economic Forum noted that the automation potential of AI enables scalability in high-transaction environments.[3] A 2024 survey by McKinsey & Company observed that users of generative AI in corporate finance reported lower costs and higher revenues, with leading firms attributing more than 10 percent of EBIT to AI.[2]
Challenges and limitations
editThe adoption of artificial intelligence in financial close also presents several challenges:
- Data quality — Inconsistent or incomplete data can lead to errors. A 2025 study reported that weak data governance increases the risk of inaccurate anomaly detection.[16]
- Model transparency — “Black box” models make auditability difficult. A 2023 study identified limited explainability as a barrier to adoption in regulated sectors.[17]
- Implementation costs — High initial investment and shortages of skilled staff remain obstacles, particularly for smaller firms.[18]
- Cybersecurity and privacy — AI systems in finance process sensitive information and require strong protections. The World Economic Forum noted in 2025 that cybersecurity risks, including deepfake fraud, are critical concerns for financial services.[3]
Adoption and industry trends
editA 2023 McKinsey survey reported that about one-third of organizations were using generative AI regularly in at least one business function.[1] Adoption accelerated rapidly in 2024, with 65 percent of organizations using generative AI early in the year and 71 percent by mid-year.[2] Analysts attributed the rise to easier-to-use interfaces and clearer business cases, particularly in finance.
In 2025, 32 percent of companies broadly adopted AI agents in financial close workflows, with trends toward continuous closes and real-time reporting, according to PwC’s AI Agent Survey cited by HighRadius.[19] Big Four firms such as Deloitte and KPMG piloted agentic AI for autonomous closes, integrating with ERP systems.[10][12] Goldman Sachs implemented generative AI for firm-wide reporting tasks, including financial close processes, improving efficiency in balance sheet preparation.[20] The World Economic Forum reported that financial services invested $35 billion in AI in 2023, projected to reach $97 billion by 2027, driven by efficiency in processes such as closing.[3]
History
editAutomation in financial close began in the 1990s with the introduction of rule-based ERP systems.[6] The use of machine learning for anomaly detection appeared in the late 2010s.[7] By 2023, generative AI was being integrated into financial institutions for tasks such as variance analysis and reporting.[21]
Timeline
editSee also
editReferences
edit- ^ a b The State of AI in 2023: Generative AI’s Breakout Year (Report). McKinsey & Company. August 2023. Retrieved 19 August 2025.
- ^ a b c "The State of AI in Early 2024: Gen AI Adoption Spikes and Starts to Generate Value". McKinsey & Company. Retrieved 8 August 2025.
- ^ a b c d Artificial Intelligence in Financial Services (PDF) (Report). World Economic Forum. January 2025. Retrieved 8 August 2025.
- ^ "Driving Efficiency Across the Journal Entry Process". Capgemini. 13 June 2022. Retrieved 15 July 2025.
- ^ "How AI in Accounting Helps Close Your Books". Workday. 18 June 2025. Retrieved 15 July 2025.
- ^ a b c Vuković, Darko B.; Dekpo-Adza, Senanu; Matović, Stefana (22 April 2025). "AI Integration in Financial Services: A Systematic Review". Humanities and Social Sciences Communications. 12 (1): 562. doi:10.1057/s41599-025-04850-8. Retrieved 12 August 2025.
- ^ a b c A Machine Learning Framework for Anomaly Detection in High-Value Payment Systems (PDF) (Report). Bank for International Settlements. May 2024. Retrieved 8 August 2025.
- ^ a b "Modern Bank Reconciliation: Templates, Automation, and AI". Accounting Insights. 15 July 2024. Retrieved 12 August 2025.
- ^ Alexander, A.; Seidmann, A. (2016). "The Impact of Emerging Technologies on Accounting and Auditing: A Structured Literature Review". International Journal of Intelligent Systems in Accounting, Finance and Management. 23 (1–2): 5–27. doi:10.1002/isaf.1386. Retrieved 12 August 2025.
- ^ a b c d e "How GenAI + People Can Transform Financial Close". Deloitte. Retrieved 12 August 2025.
- ^ "How to Transform Reconciliation Processes with AI in Fintech". Forbes. 30 January 2025. Retrieved 12 August 2025.
- ^ a b c d "AI-Enabled Financial Close as a Service". KPMG. Retrieved 12 August 2025.
- ^ "Automation to Intelligence: Agentic AI and the Finance Industry". Forbes. 4 June 2025. Retrieved 12 August 2025.
- ^ "How to Transform Reconciliation Processes with AI in Fintech". Forbes. 30 January 2025. Retrieved 12 August 2025.
- ^ Critical Capabilities for Financial Close and Consolidation Solutions (Report). Gartner. 31 March 2025. Retrieved 12 August 2025.
- ^ "Challenges and Opportunities for Artificial Intelligence in Auditing". International Journal of Accounting Information Systems. 2025. doi:10.1016/j.accinf.2025.100734. Retrieved 8 August 2025.
- ^ "Does AI Adoption Redefine Financial Reporting Accuracy". Journal of Accounting and Public Policy. 2023. doi:10.1016/j.chbr.2024.100572. Retrieved 8 August 2025.
- ^ "Closing the ROI Gap When Scaling AI". Guidehouse. 30 June 2025. Retrieved 12 August 2025.
- ^ "PwC Survey: 88% of CFOs Plan to Raise AI Budgets in 2025". HighRadius. 23 June 2025. Retrieved 12 August 2025.
- ^ "Top 25 Generative AI Finance Use Cases & Case Studies". AIMultiple. 10 July 2025. Retrieved 8 August 2025.
- ^ a b "Generative AI in Consumer Financial Services". KPMG. 15 November 2023. Retrieved 12 August 2025.
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