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Transfer pricing refers to the setting of prices for transactions between related entities within multinational corporations, which is particularly relevant for joint-stock companies operating in the global market. It plays a key role for tax purposes, as it affects the allocation of profits and the payment of taxes across different jurisdictions. The complexity of tax regulations and the growing volume of data make transfer pricing analysis increasingly demanding. Artificial intelligence (AI) offers new opportunities to streamline these processes, ranging from automation to risk prediction. In this article, we discuss how AI influences data analysis in transfer pricing, explore modern techniques and practical use cases, highlight selected insights, and outline future directions for this field.

Traditionally, transfer pricing analysis required extensive data examination to ensure that transactions between related parties were conducted in accordance with the arm’s length principle. This means that prices must reflect those that would be applied between independent entities under comparable circumstances. This process involves identifying comparable transactions or companies, which is time-consuming and prone to human error. Analysts are required to search large databases, apply multiple filters and make judgments based on both qualitative and quantitative factors. As a result, the process may lack consistency and lead to disputes with tax authorities, highlighting the growing importance of innovative solutions such as artificial intelligence (AI).

Examining the impact of AI on transfer pricing

Transfer pricing, defined as the prices applied to transactions between related entities within multinational corporations, plays a crucial role in the tax management of joint-stock companies, particularly those operating in the global market. It is significant because it determines the allocation of profits and tax liabilities across different jurisdictions, directly affecting financial performance and regulatory compliance. The complexity of global tax frameworks, such as the guidelines of the Organisation for Economic Co-operation and Development related to Base Erosion and Profit Shifting (BEPS), combined with the growing volume of data, requires the use of advanced analytical tools. Artificial intelligence (AI) is becoming a key driver of transformation in this area, offering automation, increased accuracy and improved risk management. This analysis covers modern AI techniques, practical use cases, selected insights and future perspectives, based on available sources and research.

Traditional methods of data analysis in transfer pricing

Traditionally, data analysis in transfer pricing has relied on methods such as the Comparable Uncontrolled Price (CUP) method, the Resale Price Method (RPM), the Cost Plus Method (CPM), the Transactional Net Margin Method (TNMM) and the Profit Split Method (PSM). Each of these methods requires specific data and analysis to determine arm’s length prices, with the choice of method depending on the nature of the transaction and the availability of relevant data. For example, the widely used TNMM involves comparing the net profit margin of the tested party with the margins of comparable independent companies. Identifying appropriate comparables is a critical step in this process; however, it is time-consuming and prone to human error, which often results in inconsistencies and may lead to potential disputes with tax authorities.

Modern AI techniques in data analysis

Artificial intelligence introduces a range of modern techniques into transfer pricing data analysis, improving processes and increasing their efficiency:

• Machine learning in benchmarking: One key example is the System and Method for Identifying Comparables patented by KPMG, which uses machine learning to automatically identify comparable transactions. The system analyses data such as business descriptions, financial information and SIC/NACE codes, generating similarity scores and recommendations to accept or reject potential comparables, along with clear justifications. This process was traditionally time-consuming and vulnerable to human error, issues that are significantly reduced through the use of AI, as confirmed by reports such as Potential effects of artificial intelligence on transfer pricing published by KPMG.

• Natural Language Processing (NLP): Although direct examples of NLP applications in transfer pricing remain limited, reports indicate that AI can be used to analyse legal documents, contracts and correspondence, supporting regulatory compliance. For example, PwC highlights that AI agents can review documentation to improve accuracy and compliance (AI agents: transforming the tax experience). NLP can also support the extraction of key information from large volumes of text data, which is particularly relevant in the analysis of intercompany agreements.

• Predictive and generative analytics: Firms such as Deloitte suggest that AI can be used to predict audit risks based on historical data, enabling earlier adjustments to transfer pricing strategies (Opportunities and limitations of AI in transfer pricing). Generative AI, as noted in PwC reports, can automate the preparation of reports and documentation, accelerating processes and reducing costs (AI agents: transforming the tax experience).

Examples of practical applications

• KPMG case: US Patent 11720842 B2 describes a system that uses machine learning to identify comparable companies, which is a key element of transfer pricing benchmarking. This system generates similarity scores, recommendations and justifications, reducing the risk of disputes with tax authorities (KPMG Patents Its AI Tool For Transfer Pricing).

• PwC case: AI agents are used to automate data collection and report preparation. According to available reports, this approach can reduce the time required to produce documents such as K1 forms from nearly two weeks to a single day, with plans to extend these benefits across broader tax processes (AI agents: transforming the tax experience), as highlighted by PwC.

• Deloitte and benchmarking: Reports indicate that AI solutions, such as the TPbenchmark tool, can reduce the time required for benchmarking studies by 67%, significantly improving operational efficiency, as demonstrated by Deloitte (Deloitte reduced time spent on benchmark studies by 67% with TPbenchmark).

Benefits and limitations

Benefits:

• Increased accuracy and consistency in data analysis through automation, as confirmed by reports such as AI Challenges in Transfer Pricing.

• Time savings and reduced operational costs, for example through the automation of documentation processes, as highlighted in reports published by PwC.

• Improved compliance with tax regulations through continuous monitoring of regulatory changes using AI, as supported by the AI and the transformation of tax compliance report.

Limitations:

• AI cannot replace human expertise in areas that require professional judgement, such as the interpretation of the arm's length principle, as highlighted in the Opportunities and limitations of AI in transfer pricing report.

• The risk of errors arises if AI systems are improperly trained or if the underlying data is flawed, which requires continuous human oversight, as indicated in the same report.

• Ethical and legal considerations, including data privacy concerns, may limit the use of AI, as discussed in Understanding Artificial Intelligence in Tax and Customs.

Interesting insights and unexpected aspects

One unexpected aspect is the role of tax authorities, which are increasingly using AI to detect tax fraud and enhance audit processes. For example, the Internal Revenue Service in the United States applies AI to automate internal processes, improve taxpayer services and identify potential fraud. According to available data, as many as 65% of tax administrations worldwide report integrating AI into their day-to-day operations (AI Use in Tax Administration). In Europe, countries such as France are also implementing AI solutions to combat tax abuse (France Tax Authorities Are Using AI to Clamp Down on Tax Fraud). This trend has raised concerns about a potential "AI arms race", in which companies and tax authorities compete in the level of technological advancement. Such developments may further increase the complexity of transfer pricing processes, as noted in publications such as Potential effects of artificial intelligence on transfer pricing by KPMG.

Another noteworthy example is the use of the VeRa algorithm in Italy, which cross-references financial data such as tax returns, income, property records, bank accounts and electronic payments. This system has identified more than one million high-risk cases, illustrating how AI supports the detection of potential tax fraud (Tax authorities adopt AI for tax fraud and efficiencies).

The future of AI in transfer pricing

The future appears promising, with several potential directions for further development:

• Real-time adjustments: AI may enable the automatic adjustment of transfer prices in response to market or regulatory changes, as highlighted in reports such as AI Challenges in Transfer Pricing.

• Predictive modelling: The use of AI to forecast tax outcomes and optimise transfer pricing strategies, as discussed in AI and the transformation of tax compliance.

• Dispute resolution: AI may support Mutual Agreement Procedures (MAP) and Advance Pricing Agreements (APA) by improving case management and predicting potential outcomes, as noted in publications such as Potential effects of artificial intelligence on transfer pricing by KPMG.

• Increased regulatory integration: Over time, AI may be used to continuously monitor global and local regulations, enabling companies to adapt their strategies quicker, as highlighted in reports published by PwC (AI agents: transforming the tax experience).

Summary and statistical data

In summary, AI has the potential to revolutionise data analysis in transfer pricing by offering tools for automation, process improvement and optimisation. However, to fully leverage these capabilities, companies must balance the use of AI with human expertise and pay close attention to ethical considerations. This balance is particularly important in the context of increasing regulatory requirements and potential risks. The future points towards deeper integration of AI, especially in real-time monitoring and risk prediction, which may significantly influence the transfer pricing strategies of joint-stock companies.

Statistical data confirms the growing adoption of AI in tax functions. According to a 2024 study by EY, 87% of tax and finance professionals believe that the integration of generative AI will increase the efficiency and effectiveness of their functions (How will GenAI shape tax and finance transformation?). In addition, an EY study from 2020 indicates that a typical tax team spends 40–70% of its time on data collection and manipulation, a burden that AI can significantly reduce (How artificial intelligence will empower the tax function). Furthermore, 65% of tax administrations worldwide report using AI in their day-to-day operations, highlighting its rapidly growing role (AI Use in Tax Administration).

Summary and statistical data

Company / Source	AI application	Benefits	Challenges
KPMG	System for identifying comparables (US Patent 11,720,842)	Automation of benchmarking, improved consistency, reduction of disputes	Requires high-quality data, risk of errors
PwC	AI agents for documentation and reporting	Time savings, increased accuracy, improved compliance	Need for human oversight, ethical considerations
Deloitte	TPbenchmark tool	Reduction of benchmarking study time by 67%	Regulatory complexity, need for expert judgement
Tax authorities (e.g. IRS)	Fraud detection, process automation	Improved fraud detection, enhanced audit efficiency	Risk to data privacy, transparency concerns