Accounting
Study finds the more efficient the AI, the more complex its implementation
The efficient way for accounting firms to integrate generative AI into their workflow is through robotic process automation that interfaces directly with the model’s application programming interface, though this method also requires the most expertise to implement and maintain.
This is the conclusion of a recent paper published in the American Accounting Association’s
The paper specifically named four different ways firms deploy generative AI.
The most straightforward way to do so is through a user interface with visual and interactive elements–picture ChatGPT’s web interface as an example. The paper said this method is most accessible for accounting researchers and practitioners seeking to implement LLMs, as it simply requires an internet-connected computer. It is also the cheapest in terms of access cost. At the same time, it is the least scalable and customizable of all the options and the slowest as well due to token limitations. This in mind, the study’s authors said this method is best used for client engagement and consultation, basic financial analysis and reporting and basic compliance checking.
The second is through connecting to the model directly via an API, a type of software interface enabling computer programs to communicate with each other, enabling direct passage of data. Firms can leverage an API to establish connections between their local applications/systems and the LLM service, enabling data interaction between them. This API approach can be integrated into existing workflows without significantly altering their structure, is well suited for scalable processing and allows for a greater degree of parameter setting and customization. At the same time, deployment is more complex, requiring skilled personnel to pull it off. Another limitation is the potential incompatibility of the existing workflow with API connections. The authors said some accounting tasks that benefit most from the API approach include basic financial data extraction, transaction classification and verification, and basic fraud detection.
The third is using RPA to interact directly with a traditional user interface. This allows for batch querying that the user interface method alone cannot accommodate, and is easier to integrate than the API method alone as RPA can mimic human interactions and so even if the existing system does not support underlying programming-level interaction, RPA can still connect it with the model’s user interface to enable automatic querying. Additionally, the UI-RPA method can also be combined with manual efforts that require human judgment. However, the setup is even more complex than the API method alone, and the maintenance process will also require skilled personnel who can update the bots based on changes in the user interface and the working process. Further, not every system integrates with RPA, and introducing new software might create additional privacy and cybersecurity issues, especially for accounting tasks. The authors said UI-RPA is suitable for accounting tasks such as expense management and auditing, asset management and depreciation scheduling, and budgeting and forecasting that require interaction between LLM and local systems.
The fourth is using RPA to interact with the API connected to the large language model. This is the most in-depth integration a firm could have with existing workflows, and the paper said this method maximizes the efficiency of implementing LLMs in the accounting domain. It is more efficient than even the RPA to user interface method as RPA enables the process to robotically collect raw data from existing systems by recognizing graphical-level elements and inputting them into the LLM via the API to achieve efficient queries. After the LLM’s processing, the bot can automatically retrieve the output and transmit it back to the internal systems. However, this method has all the same problems of the RPA to user interface method, but is even more difficult to set up and maintain. In general, the authors said the best use for this method is systematic financial data extraction and analysis, regulatory compliance and reporting, and trail analysis and fraud detection.
The paper found this method is the most efficient in terms of the time it takes to extract 500 unstructured financial statements. The User Interface method alone took 1,800 minutes; the API method alone took 142 minutes; the combination of user interface plus RPA took 67 minutes; and the API plus RPA approach took 42 minutes.
In terms of pure access costs, processing those 500 financial statements was just 83 cents through either the user interface or user interface plus RPA method versus $18 for the API and API plus RPA methods. However, given the time it takes to perform this task, the pure user interface method wound up being most expensive, as researchers added $52 in labor costs to those 83 cents. The API method alone, when accounting for labor costs, was the second most expensive, as the $18 access cost was combined with $31.25 in labor costs.
All this in mind, the researchers concluded that the API plus RPA method was the most efficient in terms of both time and money.
“The study finds that currently, the API-RPA is the most efficient method for large-scale accounting tasks. On the other hand, the API and API-RPA approaches are the most expensive methods to apply under the current price rate of GPT4 API,” said the paper.
However, researchers warned that the discussions of each method are based on the current level of technological development and cost.
“Some limitations might be overcome in the future with the adoption of new models. Additionally, the costs associated with each approach might change based on computing costs and market demand. Further research is needed to discuss additional application methods and cost-benefit models based on future developments of LLMs,” said the paper.
The Financial Accounting Standards Board has posted the
The FASB also announced earlier this month the availability of the
The 2025 GRT provides updates for accounting standards, including disaggregation of income statement expenses, profits interest and similar awards, and induced conversions of convertible debt instruments, and other recommended improvements.
The 2025 EBPT includes updates from the 2024 EBPT for elements specifically created for SEC Release Nos. 33–11070; 34–95025 which includes requirements for XBRL tagging of annual reports for employee stock purchase, savings and similar plans filing SEC Form 11-K.
The 2025 SRT offers improvements for elements whose underlying recognition and measurement are not specified by GAAP but are commonly used by GAAP filers and for SEC schedules related to supplemental information provided by insurance underwriters.
The DQCRT is structured from the typical design of XBRL taxonomies because it is narrowly focused on conveying the XBRL US Data Quality Committee’s validation rules, predominantly for regulator use. It isn’t intended to be used in SEC filers’ extension taxonomies. The DQCRT contains a subset of the DQC rules. The FASB Taxonomy staff evaluates the validation rules for inclusion in the DQCRT that have been available for use for more than a year, with consideration for how the DQC addressed any feedback received on a validation rule.
The 2025 MMT includes relationships focusing on accounting model information, which are viewed as helpful information for constituents. The objectives of the relationships in the MMT are to help preparers identify the proper elements for tagging their filings, assist data users in the consumption of data with additional relationship information, and assist in writing business rules that leverage the extra relationship information to help with the proper element selection and identification.
The
A federal appeals court has reversed itself, reinstating an injunction on beneficial ownership information reporting by businesses only days after lifting it.
On Monday, a panel of the U.S. Court of Appeals for the Fifth Circuit
The appeals court issued an
“The merits panel now has the appeal, which remains expedited, and a briefing schedule will issue forthwith,” said the court. “However, in order to preserve the constitutional status quo while the merits panel considers the parties’ weighty substantive arguments, that part of the motions-panel order granting the Government’s motion to stay the district court’s preliminary injunction enjoining enforcement of the CTA and the Reporting Rule is VACATED.”
Earlier this week, after the appeals court panel initially lifted the injunction, the Treasury Department
I recently analyzed 132 deals across 212 accounting firms for 2024. The 2025 predictions I’m about to share are not investment advice, so please take it with a grain of salt and use your own judgement.
With that said, let’s dive in: In 2024, private equity money flooded the accounting M&A market. Top players scooped up niche firms left and right. The $2.3 billion
It’s causing excitement and apprehension in the small-to-midmarket space (some partners are raging at outside capital).
Check out the recent wave:
• Dean Dorton’s Florida pick-up of
• Citrin Cooperman’s spree (
•
Not just big names—smaller firms too:
•
•
•
To put it in perspective:
Mid-market and regional firms are grabbing specialty shops—cannabis (
Five p𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱
• Hyper-specialization reigns: Firms will zero in on ultra-niche areas (think AI-driven forensic accounting), leaving generalists scrambling.
• Open architecture models rise: CPA firms will partner with RIAs, ERP consultants and even legal advisors to become one-stop advisory powerhouses.
• Cross-border micro-mergers: Expect global mini-deals, just like
• Tech-centric valuations: Proprietary data analytics or AI stacks will influence deal pricing more than any traditional book of business metrics.
• PE-backed succession solutions: Outside capital will transform partner retirements from liabilities into strategic exit or growth opportunities.
For some, these moves will open the door to scale, differentiate and become indispensable. For others, it’s a stark warning: adapt or risk irrelevance.
RGTI, KULR, MSTR and more
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U.S. Beneficial Ownership Information Registry Now Accepting Reports
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