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Accounting profs. adapt to AI amid cheating concerns, other challenges

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The rise of generative AI in society has also given rise to AI-guided cheating in schools, a problem that has challenged educators’ capacity to adapt. While this issue is primarily associated with the humanities, accounting educators report that they are seeing this in their own classrooms as well. 

Generative AI is known not for its skill with numbers but words, which makes it an unfortunately ideal cheating tool for humanities courses that use written essays as major components of their programs. However, while accounting is not exactly 19th century romantic literature, language and writing are not entirely irrelevant. An accounting student may not need to analyze the major themes in Ulysses, but they may be called upon to interpret an accounting standard, tax regulation or audit document, which can be just as dense and confusing. So while there are not as many opportunities for AI-guided cheating as in other fields, students are still finding places where bots can do their work for them, much to the chagrin of their professors. 

“This is definitely something I have heard quite a bit about from my colleagues in the humanities and other fields, but is becoming an issue for accounting/finance classes as well. Students still need to understand the implications of ASU’s, disclosures, etc, and if they rely entirely on AI for assignment completion that knowledge will fade away,” said Sean Stein Smith, a professor at Lehman College who teaches intermediate accounting, cost accounting, advanced accounting and forensic accounting. He also leads Lehman’s development of AI business courses, as well its crypto/blockchain content.

AI cheating
Kyoto city, Japan – May 05, 2023: The OPEN AI logo visible on a smartphone screen

filins – stock.adobe.com

He added that he has seen AI-guided cheating first-hand, especially for short-form essay assignments as well as when he requires students to perform financial analyses using specific ratios. 

Douglas Carmichael, former chief auditor of the PCAOB and currently a professor at Baruch College where he teaches auditing, noted that while he does not himself give any writing assignments that a student could use generative AI to cheat on, this doesn’t mean they’re still not using AI to undermine the purpose of an assignment, though once students realized he was on to them it has become less of an issue. 

“I do ask students to submit at least one question before class on something in the text or recorded lectures they found difficult to understand or want additional information about. My experience in prior semesters was that about half of the students submitted a question that seemed suspicious to me given the language used and generality of the issue. The lack of specific reference to the topic in the text or recorded lecture was also apparent. These kinds of questions did not earn any credit and as word got out about that use of ChatGPT is infrequent,” he said. 

But even if students are not out and out cheating, some have observed an unhealthy reliance on generative AI starting to form. Jack Castonguay, vice president of learning and development with Surgent as well as a Hofstra University professor who teaches advanced courses in accounting and auditing theory, has seen students struggling with understanding and communicating core concepts at least in part due to their reliance on generative AI. 

“We see the reliance significantly when they have to give a presentation or take an in-person exam. It’s clear they have gotten to that point by using AI and can’t apply the logic on their own. Maybe in 3-10 years (given the speed of the improvement in LLMs) they won’t have to do it on their own, but it’s a large problem now for client relationships and having conversations with this in practice. They need to look up everything and use AI as a crutch. Seminar discussions are like pulling teeth oftentimes for me,” he said. 

With this in mind, accounting educators — much like those in other fields — are currently in conversation about how to respond to this issue. Richard C. Jones, a Hofstra University accounting professor and former technical staff member at the Financial Accounting Standards Board, said this is a major topic of debate and discussion among college faculty and administrators, noting that it seems to be brought up in nearly every meeting. It is obvious, he said, that students will use LLMs on assignments, and so therefore the challenge for faculty is to assign projects and papers that require students to actually demonstrate their knowledge versus just handing in a paper or presentation. 

“Fortunately, I teach classes that require the application of accounting rather than accounting theory. Therefore, my exams and other assessments are specific to case information provided and application of the accounting rules in providing the journal entries and the related disclosure information. So, my students do not have as much of an opportunity to use LLMs to answer the questions,” he said. 

Additionally, he mentioned that educators are trying to find ways to work AI into their assignments, considering how quickly accounting firms themselves have taken to it. 

Tracey Niemotko — a Marist University professor who teaches accounting and auditing as well as sustainability, taxation and forensic accounting — said that she views AI as more of a tool than a cheating mechanism, pointing out how models can be used to expedite audit procedures or clear away the busy work that eats up the day of many professionals. Consequently, she is a little more sanguine about AI-guided cheating, noting that even if students do use AI in their assignments, the nature of the work makes cheating difficult. 

“Even with electronic testing in the classroom, I do not see cheating as a concern overall. I think the accounting students are perhaps a bit more disciplined than most students, so I don’t think they have the mindset to cheat. Even for writing assignments in my upper-level accounting courses, students may use AI to assist them, but they are required to write ‘in their own words.’ Overall, the majority do their own written work but may use AI as a tool to help them develop an outline or get them started,” she said. 

Abigail Zhang Parker, a University of Texas at San Antonio professor whose research specialty is AI in accounting, has also directly worked AI into her classes. For example, her Accounting Information Systems courses include hands-on workshops where students learn to operate different accounting software solutions. She noted that AI can be a useful tool for finding relevant information and understanding difficult concepts.

Therefore, her overall philosophy is that students can use generative AI to help with assignments but not on exams, as that is when they’re tested on their actual understanding of the topic. So long as it is only used for assignments versus exams, she does not consider using AI to be cheating. She added it would be impractical to prevent the use of AI entirely anyway, it’s better for educators to find ways to use it too. However, she noted that teaching students proper use of AI can, itself, present a challenge. 

“Perhaps we need to guide them how to use it properly. This is not easy. One method that came to my mind is to make the parts that demonstrate students’ own skills take a greater portion in the grading components.  … For example, there are three exams throughout the semester, and they take 60% of the total grade, while assignments take 10%. For classes where students need to submit a report and make a presentation, maybe the report itself will not take up a high portion of the grade, but the in-person presentation will, as it better reflects students’ true understanding of the subject. And once students know that they will be mainly graded on their own performance, they are more incentivized to think through the problem than simply over-relying on AI,” she said.

Another reason to learn AI in the classroom is that, once students are working as professional accountants, clients will likely be using AI as well, and they will need to understand and explain what is missing from the AI’s answers. However, Castonguay, from Hofstra, voiced concerns that over-reliance on AI is eroding the critical thinking and reasoning skills needed to properly evaluate these answers in the first place. He does an exercise in class where students have ChatGPT summarize a FASB ASU and review its findings. Some, he said, don’t even know where to start as they have obviously been relying on ChatGPT to understand it at all. 

“My bigger concern is [that] by such a reliance on AI they will lack the critical thinking and synthesizing skills that are still valued even with AI. To use a sports analogy, they are only bowling with gutter guards – what happens when those aren’t there?” he said. 

Smith, from Lehman, said these kinds of things underscores the need to teach responsible AI usage in a way that does not degrade the human skills that they’ll be relying on in the professional world. He felt, unfortunately, that this could be an uphill battle. 

“I do think that as AI becomes more integrated into the classroom and profession, we are going to have to really double-down on making sure students still have the ability to think critically. Especially in cases where questions or data may change on-the-fly, students are seeming to have a harder time pivoting and adapting to analyze said data on the spot. It’s a growing problem with no cookie-cutter or easy solution, but is definitely something I know is being talked about in pretty much every accounting department/School of Business,” he said. 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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