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DeepSeek on par with GPT, Claude, Llama for accounting

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Accounting artificial intelligence leaders have deemed Chinese large language model DeepSeek as roughly on par with other general models when it comes to knowledge, questions and tasks relevant to the profession. 

DeepSeek’s latest R1 model was released to the world last week to much fanfare by producing performance comparable to massive models like Claude or ChatGPT but at a fraction of the cost — something that blindsided other players in the AI field who hadn’t believed it was possible. In the short time since it exploded on the world stage, industry observers have had to rethink their priors, as the event has shown that one does not have to be a gigantic corporation like Microsoft (whose $3 trillion market cap is bigger than the entire GDP of Italy) to produce quality AI models. 

Generative AI models are not usually known for their math skills, let alone accounting, as the probabilistic nature of their outputs makes precision difficult. While theoretically someone could apply public models like ChatGPT to an accounting problem, the possibility the AI could make up information from whole cloth makes it a risky proposition. This does not, however, mean they’re completely incapable of performing accounting tasks, just not as good as a more specialized solution. 

Robot reading books - claymation style

Alexandr Vasilyev – stock.adobe.

Jeff Seibert, CEO of accounting automation solutions provider Digits, tested DeepSeek against OpenAI’s ChatGPT, Anthropic’s Claude and Meta’s Llama and found its ability to perform accounting tasks and answer relevant questions to be roughly on par with the others. He asked the AIs to classify 1000 transactions, given the description, dollar amount and a chart of accounts. ChatGPT was correct 61-65% of the time depending on the specific version; DeepSeek was correct 59.90% of the time; Llama was correct 48% of the time; and Claude was correct 43.4% of the time. The correct category was in the top 3 suggestions 75-78% of the time for ChatGPT (depending on version), 69.67% for DeepSeek, 61% of the time for Llama and 56% of the time for Claude. 

ChatGPT created a fake category 0.10-4% of the time (depending on version), Llama did so 0.2% of the time, Claude 2.8% of the time, and DeepSeek 0.39% of the time. As far as speed, it answered queries faster than GPT-o1 mini, GPT-o1, and Llama. It was slower than GPT-4o, GPT 4 Turbo and Claude. 

Seibert, in a LinkedIn post outlining his experiment, noted that even if it doesn’t outdo the other models in all areas, the fact that it was made at a fraction of their cost is quite impressive. 

“If their claims around training cost are accurate, this represents a massive breakthrough in model efficiency and sets the new bar for open source AI performance,” he wrote. 

Daniel Shorstein, president of technology solutions advisory firm James Moore Digital, also put DeepSeek through its paces by asking multiple choice questions covering a range of accounting topics, adding he tried to keep them just difficult enough that it would trip up a less than highly intelligent LLM. He used the same test as the one he made to evaluate Llama, Claude, ChatGPT and other models against each other. He found its results to be inconsistent. 

He illustrated with how it reacted to a question on segregation of duties: 

Question: “There are three employees in the accounting department: payroll clerk, accounts payable clerk, and accounts receivable clerk. Which one of these employees should not make the daily deposit? A. payroll clerk B. account payable clerk C. accounts receivable clerk D. none (any can make the deposit)”

DeepSeek, like other models lately, is equipped to not only provide an answer but reveal some of its internal reasoning in how it got to the answer. Shorstein noted that, internally, it actually got the correct answer after a long chain of reasoning where it first recalled general principles of segregation of duties, thought of an ideal setup, thought of possible exceptions and special circumstances, went back to the main point of segregation of duties and ultimately determined the answer was C, the accounts receivable clerk, which Shorstein said was the correct answer. 

“But its final answer: ‘The correct answer is A. payroll clerk,'” he said in a message.

Meanwhile, Hitendra Patil, CEO of accounting tech consultancy Accountaneur, said DeepSeek gives more detailed answers compared to ChatGPT, and also appears to have been built to not only answer the direct question asked, but to actually pre-empt the likely follow-up question that the user may ask after the first question and provides answers to such pre-empted follow-up questions. He added that it also goes over its reasoning when discussing math questions versus other models which simply give an answer.

For example, he asked the model what is 343 multiplied by 741. It broke down the problem using the distributive property to simplify the multiplication, then added the results together to get 254,163. 

At the same time, he noted that DeepSeek does not browse the Internet unless specifically asked, and so some of the answers it gave him about tax law were somewhat dated, versus ChatGPT which more or less gave the latest information. Overall, he said that it seems slightly behind ChatGPT for now, despite rumors that it had been trained similarly.  

“There is no verifiable proof … but DeepSeek has been/is being trained on similar underlying data that ChatGPT was/is being trained on, albeit it seems to be lagging behind ChatGPT,” he said in an email.

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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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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.

Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.

Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.

Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.

This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.

Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.

By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.

Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.

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