Draft:AI in financial close
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 9 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 21 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
[edit]Artificial intelligence (AI) in financial close refers to the use of machine learning (ML), natural language processing (NLP), and generative AI to automate and enhance tasks in the financial close process, such as account reconciliation, journal entry preparation, and variance analysis, performed at the end of a reporting period. A 2024 McKinsey survey reported that 65% of organizations adopted generative AI in at least one business function, with finance workflows like corporate finance showing high usage and productivity gains, including cost decreases and revenue increases.[1] In 2025, the World Economic Forum noted that financial services, including closing processes, have high automation potential, with 32–39% of tasks suitable for full automation.[2]
Overview
[edit]The financial close process consolidates an organization’s financial data to produce compliant reports, involving account reconciliation, journal entries, and financial statement preparation.[3] Manual methods, using spreadsheets and sequential approvals, often lead to delays and errors.[4] AI has been integrated since the late 2010s to automate repetitive tasks and improve accuracy through anomaly detection and predictive analytics.[5]
Technologies used
[edit]AI technologies in financial close include:
- Machine Learning (ML): Identifies anomalies and automates journal entries by analyzing historical data. A 2024 study found a supervised ML algorithm achieved a 93% detection rate for anomalies in payment systems.[6] A 2024 Accounting Insights article described AI-driven reconciliation tools that automate anomaly detection and validation in financial close processes.[7]
- Natural Language Processing (NLP): Extracts data from unstructured sources like invoices and contracts, reducing manual processing time.[8] A 2024 report noted NLP within generative AI streamlines accrual scripting and data extraction.[9]
- Generative AI (GenAI): Produces variance explanations and task lists. Deloitte noted in 2024 that GenAI enhances workflow automation.[9] A 2025 Forbes Technology Council article highlighted GenAI’s role in automating reconciliation in fintech, reducing errors through anomaly detection.[10]
- Agent-Based AI: Manages multi-step workflows like exception routing. KPMG’s 2025 Intelligent Close leverages agentic AI for autonomous accounting.[11] A 2025 Forbes Finance Council article noted agentic AI’s role in automating complex financial workflows, including autonomous closes.[12]
Comparison with Traditional Approaches
[edit]| Aspect | Traditional Methods | AI-Enabled Methods | Source |
|---|---|---|---|
| Data Reconciliation | Manual spreadsheet matching, error-prone | Automated anomaly detection, real-time validation | [7][13] |
| Journal Entries | Manual input, sequential approvals | Predictive suggestions, automated entries | [9] |
| Close Cycle Time | 10–15 days (average) | 6 days or less with high automation | [14] |
Benefits
[edit]AI in financial close shortens cycle times and reduces errors. A 2024 Deloitte report found that GenAI supports the financial close process by automating tasks and improving audit readiness through domain-specific knowledge bases.[9] KPMG's 2025 Intelligent Close highlights reduced human intervention and risk of error through AI-driven anomaly detection.[11] The World Economic Forum highlighted in 2025 that AI’s automation potential supports scalability in high-transaction environments.[2] A 2024 McKinsey survey noted that generative AI users in corporate finance reported cost decreases and revenue increases, with high performers attributing over 10% of EBIT to AI.[1]
Challenges and limitations
[edit]AI adoption faces challenges:
- Data Quality: Inconsistent data can lead to errors. A 2025 study noted that poor data governance risks inaccurate anomaly detection.[15]
- Model Transparency: “Black box” models hinder auditability. A 2023 study identified explainability as a barrier in regulated sectors.[16]
- Implementation Costs: High initial investments and talent shortages challenge smaller firms.[17]
- Cybersecurity and Privacy: AI systems handling sensitive financial data require robust protections. The World Economic Forum noted in 2025 that cybersecurity risks, like deepfake fraud, are critical in financial services.[2]
Adoption and industry trends
[edit]In 2024, 71% of organizations adopted generative AI in at least one function, up from 65% earlier in 2024 and 33% in 2023, with finance functions like corporate finance seeing increased use, per a McKinsey survey.[1] In 2025, 32% 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.[18] Big Four firms like Deloitte and KPMG piloted agentic AI for autonomous closes, integrating with ERP systems.[9][11] Goldman Sachs implemented generative AI for firm-wide reporting tasks, including financial close processes, improving efficiency in balance sheet preparation.[19] 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 like closing.[2]
History
[edit]Automation in financial close began in the 1990s with rule-based ERP systems.[5] Machine learning for anomaly detection emerged in the late 2010s.[6] By 2023, generative AI was integrated for variance analysis and reporting in financial institutions.[20]
Timeline
[edit]- 1990s: Rule-based ERP systems automate reconciliations.[5]
- 2018–2020: Machine learning applied for anomaly detection.[6]
- 2023–2025: Generative and agentic AI adopted for autonomous closes.[20][11]
See also
[edit]- Accounting software
- Artificial intelligence in finance
- Big Four accounting firms
- Enterprise resource planning
- Explainable artificial intelligence
- Financial close management
- Intelligent automation
- Internal control
- Machine learning
- Robotic process automation
References
[edit]- ^ 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.
Category:Artificial intelligence Category:Accounting software Category:Automation Category:Financial management
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