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Each edition delivers one clear, evidence-backed idea you can use. This week: Proven AI Use Cases with a clear return for finance teams

Most finance teams already have artificial intelligence (AI) somewhere in the building. Copilot in Excel. A vendor bolting a “smart” module onto the enterprise resource planning (ERP) system. Somebody’s rogue ChatGPT tab. What’s missing is a sober read on which finance workflows the peer-reviewed literature says AI actually improves — and which ones are still hype.

Six recent peer-reviewed studies converge on where AI pays off inside internal finance workflows — and where it doesn’t.

Invoice coding and accounts payable (AP). Bardelli et al. (2020) trained multiclass classifiers on Italian e-invoice data and predicted general-ledger (GL) account and value-added tax (VAT) codes directly from invoice fields, with accuracy strong enough to route most invoices without human touch (Bardelli et al., 2020).

Anomaly detection in the general ledger. Bakumenko and Elragal (2022) tested seven supervised and two unsupervised machine learning (ML) models on a real GL dataset. Both approaches surfaced predefined anomaly types and produced risk-ranked samples of journal entries — targeted sampling that random selection cannot deliver (Bakumenko & Elragal, 2022).

Cash flow forecasting. Drydakis (2022) surveyed 317 UK small and medium-sized businesses (SMBs) across two waves during COVID and found that AI cash flow forecasting was associated with measurably reduced business risk. The mechanism was dynamic capability — AI let leaner teams pivot faster (Drydakis, 2022).

Robotic process automation (RPA) for data-entry drudgery. Parker and Appel (2021) ran a six-month action-research study inside a financial services firm’s team using an ML-based RPA solution. Productivity rose, roles were redefined toward analytical work, and employee sentiment improved (Parker & Appel, 2021).

Large language models (LLMs) for financial analysis and disclosure review. Liu et al. (2024) evaluated ChatGPT-4o on multi-step financial reasoning tasks against human analysts. The model handled basic and moderate analytical work capably but degraded on deep critical-thinking tasks in specialized finance areas — arguing for AI-plus-human, not AI-alone, workflows (Liu et al., 2024).

The state of the evidence. Dong et al.’s (2024) scoping review in the International Journal of Accounting Information Systems synthesized the LLM-in-accounting literature and grouped useful applications into three buckets: task automation (classification, summarization), research assistance (data extraction), and audit/analysis support — while flagging accuracy variance and prompt sensitivity as open problems (Dong et al., 2024).

The common thread across all six: AI earns its keep on high-volume, rule-heavy, evidence-producing work. It falters when the workflow demands judgment, context, or negotiation.

Enterprise finance teams pilot these tools with a data engineer and a change manager. SMB finance teams have a controller, one AP clerk, and QuickBooks Online.

Vărzaru (2022) surveyed 396 managerial accountants and found that resistance to change, organizational culture, low trust, and technology cost were the dominant barriers to AI adoption — precisely the constraints that hit hardest below $50 million in revenue (Vărzaru, 2022). The same study found that perceived ease of use and automation depth drove acceptance. Translation: SMB finance teams will adopt AI when the tool disappears into an existing workflow, and reject it when it becomes a second system to babysit.

The trap for small finance shops is the wrong starting point. Teams reach for financial planning and analysis (FP&A) copilots because they sound strategic, then abandon them when the model can’t beat a well-built spreadsheet. Meanwhile, the AP inbox — where the research shows the clearest wins — stays manual.

Start with a two-question filter drawn from the studies above:

If both are yes, it’s a candidate. If either is no, park it.

Then run a 90-day pilot on one workflow, using the sequence Parker and Appel documented — measure a baseline, deploy, redefine roles, remeasure (Parker & Appel, 2021):

For anomaly detection and generative AI (GenAI)-assisted analysis specifically, Bakumenko and Elragal’s and Liu et al.’s findings converge: use the model to produce a risk-ranked sample or a first-draft narrative for the controller to review — not to auto-approve entries or publish analyses (Bakumenko & Elragal, 2022; Liu et al., 2024). The controller stays accountable. The model just points the flashlight.

The finance teams pulling ahead aren’t the ones with the flashiest AI stack. They’re the ones who picked a boring, high-volume workflow, measured before and after, and let the humans move up the value chain. The research has been telling us where to start for five years. The invoices are still waiting.

Bakumenko, A., & Elragal, A. (2022). Detecting anomalies in financial data using machine learning algorithms. Systems, 10(5), 130. https://doi.org/10.3390/systems10050130

Bardelli, C., Rondinelli, A., Vecchio, R., & Figini, S. (2020). Automatic electronic invoice classification using machine learning models. Machine Learning and Knowledge Extraction, 2(4), 617–629. https://doi.org/10.3390/make2040033

Dong, M. M., Stratopoulos, T. C., & Wang, V. X. (2024). A scoping review of ChatGPT research in accounting and finance. International Journal of Accounting Information Systems, 55, 100715. https://doi.org/10.1016/j.accinf.2024.100715

Drydakis, N. (2022). Artificial intelligence and reduced SMEs’ business risks: A dynamic capabilities analysis during the COVID-19 pandemic. Information Systems Frontiers, 24(4), 1223–1247. https://doi.org/10.1007/s10796-022-10249-6

Liu, L. X., Sun, Z., Xu, K., & Chen, C. (2024). AI-driven financial analysis: Exploring ChatGPT’s capabilities and challenges. International Journal of Financial Studies, 12(3), 60. https://doi.org/10.3390/ijfs12030060

Parker, H., & Appel, S. E. (2021). On the path to artificial intelligence: The effects of a robotics solution in a financial services firm. South African Journal of Industrial Engineering, 32(2), 37–47. https://doi.org/10.7166/32-2-2390

Vărzaru, A. A. (2022). Assessing artificial intelligence technology acceptance in managerial accounting. Electronics, 11(14), 2256. https://doi.org/10.3390/electronics11142256