AI in Transfer Pricing: How Artificial Intelligence is transforming transfer pricing compliance and analysis

By: Joanne Lesley C. Padilla

"Transfer pricing remains a discipline grounded in 50% economics, 30% law and regulation, and 20% negotiation—a balance that AI cannot replicate. It cannot interpret business motivations, assess economic substance, navigate Philippine regulatory nuances, or represent taxpayers before the BIR during an audit. These responsibilities require professional expertise, skepticism, and practical experience that only human practitioners can provide."

As a transfer pricing professional, I have long been hesitant to fully embrace AI, especially with the growing perception that it may eventually take over parts of our practice. My reservations, however, do not change the reality that is becoming increasingly clear — AI is here, and it is reshaping our profession.

While my initial reaction was to view AI as a threat, I have come to see that it offers a silver lining. In this article, I will share why AI should be regarded as a partner rather than a competitor, and what this transformation means for TP professionals.

1) Introduction

Digitalization is no longer a distant concept—it is already reshaping the way we live and conduct business. The COVID-19 pandemic accelerated this shift, pushing, if not compelling, multinational enterprises to rely heavily on digital tools to move goods and deliver services in an environment where face-to-face interactions were limited. While digitalization has continued to transform businesses into more agile entities, it has also brought with it another set of transfer pricing risks—especially issues involving intellectual property (IP).

At the center of this digital transformation is Artificial Intelligence (AI). AI is rapidly redefining how businesses operate, and the field of transfer pricing is no exception. Traditionally, transfer pricing analysis has been a labor-intensive process—from crafting search strategies, to screening companies, to identifying and selecting comparables. These tasks are repetitive, time-consuming, and often prone to human error. With the introduction of AI, many of these routine processes can now be streamlined, accelerating turnaround times and significantly reducing the likelihood of mistakes. This allows transfer pricing professionals and tax authorities alike to focus their efforts on more complex, judgment-based issues that require human expertise.

The benefits of AI are clear. Yet its rise brings forward important questions: Should we rely entirely on AI-generated outputs? And equally important—where do we, as professionals, fit into this evolving landscape?

2) Current Challenges in Traditional Transfer Pricing in the Philippines

Traditional transfer pricing work remains heavily manual, especially in jurisdictions like the Philippines where data availability is limited. A typical benchmarking cycle involves:

a. Data gathering
b. Search process
c. Determination of the arm’s length range
d. Documentation

Among these, the search process—or comparability analysis—is the most time-consuming and error-prone. This phase requires selecting and screening hundreds of companies in a dataset, often around 350, to identify which ones qualify as comparables. Once selected, the financial statements of these companies must then be retrieved from the Philippine Securities and Exchange Commission before calculating the arm’s length range.

Commercial databases such as Bureau van Dijk’s TP Catalyst help automate parts of this search process, and recent AI-driven enhancements have made screening and refining comparables significantly more efficient. However, Philippine financial data in TP Catalyst remains largely outdated or unavailable. As a result, practitioners often resort to regional comparables—a challenge in itself, given the tax authority’s preference for local comparables whenever possible. Practitioners must justify the use of foreign datasets, provide detailed screening procedures, and support every analytical judgment.

Aside from the issues on the search process, the preparation of transfer pricing documentation depends heavily on judgement. Functional analyses, method selection rationales, comparability narratives, and economic conclusions vary significantly across transfer pricing advisors, resulting in inconsistencies that demand extensive review.

AI helps bridge these gaps. By automating routine tasks, enhancing consistency, and reducing the risk of human error, AI allows transfer pricing professionals to focus on strategic interpretation, defensible analysis, and audit-ready documentation.

3) Key Applications of AI in transfer pricing

The previous sections outlined the challenges of traditional transfer pricing processes. This section, in contrast, highlights how AI addresses these issues and the benefits it brings across the transfer pricing workflow.

a. Automated Benchmarking & Comparable Screening
AI algorithms can:
• Read and extract financial information faster from large datasets
• Classify companies into industry codes more accurately
• Apply inclusion/exclusion criteria consistently across years
• Generate comparable tables, PLI calculations, and statistical ranges automatically
• Re-run updated searches for financial updates
AI’s predictive capabilities can significantly shorten turnaround times. AI-assisted searches also help reduce the risk of inconsistencies when reviewing the business descriptions of companies in a search set.

b. FAR Analysis Automation
AI can assist in organizing data for functional analysis, detecting inconsistencies in entity characterization, and even suggesting improvements in the functions-assets-risks (FAR) matrix. It can also support practitioners in mapping value creation, particularly in TP studies involving intellectual property.
However, AI outputs are not 100% accurate. Because AI relies solely on the information provided to it, its reliability is only as strong as the data it receives. FAR analysis also requires significant professional judgment—something AI cannot fully replicate.

c. Drafting of TP Documentation
AI can assist in drafting key sections of transfer pricing documentation, including executive summaries, industry overviews, benchmarking narratives, methodology explanations, functional analyses, and conclusions. Templates can also be pre-programmed to follow BIR-required formatting. While human review remains essential, AI significantly reduces the time needed to prepare these sections.

4) Limitations

While the advantages of AI in transfer pricing are undeniable, practitioners must still proceed with caution when using it. AI should be viewed as a complementary tool rather than a substitute, as transfer pricing continues to rely heavily on professional judgment and nuanced economic reasoning. AI cannot replicate the analytical thought process required to address complex TP issues, and its output is only as reliable as the information provided by the user. Poor-quality or incomplete data will inevitably lead to poor-quality results without proper human validation.

Confidentiality is another critical concern. TP professionals handle highly sensitive financial and operational information, and in an environment where data breaches are increasingly common, clients may be uncomfortable with excessive reliance on AI tools. Clear safeguards and responsible-use protocols are essential to maintain trust and protect confidential information.

Ultimately, AI should be treated as an aid—not an authority. It can enhance efficiency and consistency, but it must be applied critically and always supported by the technical expertise and informed judgment of experienced TP professionals.

5) Conclusion

To return to the question posed at the beginning of this article—Should we rely entirely on AI-generated outputs?—the answer is clearly no. While AI is highly effective at handling routine and repetitive tasks that often consume a significant portion of a practitioner’s time, it cannot replace the human judgment required for complex transfer pricing issues. Its reliability is entirely dependent on the quality of the data it receives; incomplete, inaccurate, or poorly structured inputs will inevitably produce flawed results. Moreover, AI may generate incorrect assumptions, overlook subtle but important details, or even produce information that appears credible but is factually incorrect. Without careful human review, these shortcomings can undermine the defensibility of TP documentation, particularly during BIR audits where accuracy, context, and sound reasoning are essential.

Transfer pricing remains a discipline grounded in 50% economics, 30% law and regulation, and 20% negotiation—a balance that AI cannot replicate. It cannot interpret business motivations, assess economic substance, navigate Philippine regulatory nuances, or represent taxpayers before the BIR during an audit. These responsibilities require professional expertise, skepticism, and practical experience that only human practitioners can provide.

In this evolving landscape, practitioners still hold the helm. Although AI has automated many tasks previously performed manually, it simultaneously creates more room for professionals to focus on higher-value, strategic functions that demand critical thinking and industry insight. Rather than viewing AI as a threat, we can view it as an opportunity—one that allows us to elevate the quality of our work, deliver deeper advisory value, and become even more effective partners to our clients.

The article is for general information only and is not intended, nor should be construed as a substitute for tax, legal or financial advice on any specific matter. Applicability of this article to any actual or particular tax or legal issue should be supported therefore by a professional study or advice. If you have any comments or questions concerning the article, you may e-mail the author at This email address is being protected from spambots. You need JavaScript enabled to view it. or call 8403-2001 local 310