The Asymmetry in AI Use Between Tax Authorities and Taxpayers in Transfer Pricing Defense

A recent Tax Notes article on a Turkish transfer pricing case traces the dispute through to its downstream effect on a U.S. foreign tax credit claim. In doing so, it sets out an asymmetry that deserves more attention in transfer pricing practice: Turkey regulates how professional advisors may use generative AI in giving tax advice, but places no comparable constraint on its own tax administration's use of machine analysis for risk-scoring, flagging related-party anomalies, or selecting cases for audit. The rules on the taxpayer side are specific and restrictive. The rules on the authority side are largely unwritten.

The same week the Turkey piece appeared, a Manila-based practitioner column described how the Philippines' Bureau of Internal Revenue has operationalised a system-assisted, risk-based audit selection framework under Revenue Memorandum Order 1-2026. The indicators are aimed squarely at related-party transactions: persistent losses against strong revenue, tax-to-sales ratios that look too low, and heavy reliance on a single related counterparty. Under this framework, transfer pricing risk is pre-flagged by the system before an examiner opens the file. Read together with Turkey's dual posture, restrictive on taxpayer-side AI reliance and expansive on authority-side machine analysis, the two examples suggest the asymmetry is not confined to one jurisdiction.

The same question arises for anyone advising Indian multinationals or GCCs. India's safe harbour election process is moving toward an automated, rules-based model, CBDT's compliance apparatus already uses AI-assisted risk profiling, and the APA and audit infrastructure is becoming steadily more analytics-driven. All of this sits on the authority side of the same asymmetry described in the Turkish and Philippine examples. Several Big 4 and boutique TP technology vendors have published material this year describing increased use of generative AI in preparing local files, benchmarking memoranda, and functional analyses. Whether that assistance will be treated on the same footing as a signed opinion from a human expert, when the question becomes whether a position was taken in good faith or whether a reasonable-cause defence survives scrutiny, is not yet settled by any rule or ruling in India.

The Turkish case does not resolve this question. Its value lies in naming the gap explicitly, rather than treating AI in tax administration as a single undifferentiated trend of efficiency gains for everyone. Whether the standards governing reliance, documentation, and reasonable cause will develop in step for both tax authorities and taxpayers, or whether taxpayers will end up defending AI-informed positions against AI-generated risk scores under rules drafted for one-sided use, remains open.

For Indian practitioners, the practical implication is to document how AI tools are used in preparing local files, benchmarking analyses, and functional analyses now, before the question is tested in audit or litigation, so that the basis for any position can be explained and defended independently of the tool used to generate it.

India's Automated Safe Harbour Approval and the Administrative-Law Question Canada Is Now Asking About AI-Driven Audit Selection

Budget 2026 moved Safe Harbour approval for IT services onto an automated, rule-driven framework and removed the requirement for an officer to examine the application. Applicants who meet the prescribed margin thresholds receive approval without human review, and the outcome is locked in for up to five years.

A recent Canadian Tax Journal paper by Theertha Narayanan and Pramod Kumar Siva examines a related but distinct question: whether the Canada Revenue Agency's use of AI-based risk scoring to select transfer-pricing files for audit must satisfy the same procedural-fairness standard that Canadian courts apply to other administrative decisions. The paper argues that it should, particularly after Canada's 2025 federal budget introduced stricter TP methodologies, expanded recharacterization powers, higher penalty thresholds and shorter documentation deadlines, which raised the consequences of being selected for audit. On the paper's argument, the selection decision itself should be reviewable under the reasonableness standard set out in the Supreme Court of Canada's Vavilov framework: justified, transparent and intelligible.

The Canadian debate concerns whether an algorithm can lawfully flag a file for human review. India's Budget 2026 reform goes further: it has let an algorithm replace the human reviewer for a determination that carries up to five years of certainty on arm's length pricing. Indian commentary on the change does not appear to have framed it as raising a comparable administrative-law question.

The Canadian paper's argument rests on specific case law rather than general concerns about AI. It draws on Vavilov's requirement that administrative reasoning be internally coherent and "justified in relation to the facts and law that constrain the decision maker," and on Dow Chemical's confirmation that discretionary CRA decisions under the Income Tax Act are reviewable on that same reasonableness standard. The paper's contribution is to apply these doctrines to a system that produces a risk score rather than a chain of reasoning, and to ask whether a score alone can meet a standard that requires justification.

The Indian changes are comparably specific. Budget 2026 pushed Safe Harbour approval into what industry commentary has called an "auto-pilot mode": applications are processed through a fully automated framework without officer discretion, in exchange for locking in outcomes for five consecutive years. The stated rationale, reducing subjective review, reducing litigation and increasing predictability, is consistent with the reasoning most jurisdictions offer for automating tax administration. Automating an approval, however, is a different act from automating a flag for human review, and that difference is what the Canadian paper is built to examine.

The practical stakes for Indian practice are concrete. GCCs are the primary beneficiaries of the new Safe Harbour automation, and the pitch to them has centred on certainty: file the declaration, receive automatic approval, and bypass officer review. An approval granted without examination is, in substance, an algorithmic determination of an arm's length outcome. It is made without safeguards that the international literature on automated decision-making treats as significant, including explainability, an audit trail showing why a given margin was accepted, and a documented basis for treating a taxpayer's declared facts as sufficient without human verification.

India has no published equivalent of the Vavilov standard for testing whether a rule-driven tax decision is reasonable. Nor is there public discussion, so far, of what a taxpayer can do if an automated approval later turns out to rest on a misclassification the system had no way to detect. On most commentary on the reform, officers who did not examine the application at the front end can still reopen the matter on audit later. That leaves taxpayers with a certainty that appears binding on its face but may not bind the department in substance, because no human made a reviewable decision at the outset.

Whether Indian tax administrative law needs an equivalent of the reasoned-decision requirement that Canadian courts are now applying to algorithmic audit selection remains open. If Budget 2026 signals a broader shift toward rule-driven, officer-free approval extending to APA processing or audit selection, the question will carry more weight, though the current material does not establish how far that shift will go. There is also a defensible counter-view, that Safe Harbour was always meant to operate as a self-assessment regime in which the absence of officer discretion is a deliberate feature rather than a due-process gap. Practitioners advising on Safe Harbour elections should flag to clients that automated approval does not necessarily foreclose later audit scrutiny, and should treat the administrative-law framing, not merely the compliance mechanics, as part of the risk assessment.

From Annual Documentation to Continuous Monitoring: What the Nexdigm-infer360 Alliance Signals for TP Practice

Transfer pricing documentation in India is built after the fact. Benchmarking studies are prepared once a year, local files are frozen at a point in time, and the arm's length position a taxpayer defends before a TPO reflects a snapshot taken months or years after the transactions occurred. The analysis need not be wrong, but it is backward-looking by design: assembled once the business has already priced the transaction, to justify a decision already taken.

A new generation of AI-native platforms is pitching something different: continuous, always-on monitoring of intercompany pricing through the year, rather than faster annual documentation. Nexdigm, the Mumbai-headquartered advisory firm, has recently announced an alliance with infer360, a Singapore-based AI platform built by former Big Four transfer pricing partners. The stated aim is to help clients move from periodic compliance to continuous transfer pricing management: automating documentation, running ongoing risk monitoring, and maintaining an audit-ready trail through the year rather than reconstructing one afterward.

The marketing language is unremarkable; most vendors in this space now describe themselves as AI-native. What is worth noting is that a serious Indian-origin advisory practice with established APA, controversy and operational TP credentials is putting its own resources behind the view that continuous monitoring, rather than faster annual studies, is where the market is heading.

This sits alongside a parallel move on the US side of the industry, where an established transfer pricing technology vendor's benchmarking and documentation AI suite continues to draw trade-press attention as a template for how mid-market and boutique practices are narrowing the technology gap with the Big Four.

The relevance for practitioners lies in the kind of evidentiary record these platforms are built to produce: a clean, time-stamped account of the methodology used and how it performed against comparables through the year. That is the kind of record Indian tribunals have shown they will act on.

In a Delhi ITAT ruling this month in Honda R&D (India)'s case, the taxpayer faced a ₹50.20-lakh adjustment for AY 2020-21 and pointed the Tribunal to a TPO order for the following assessment year, AY 2021-22, in which the department had accepted the identical benchmarking position it was disputing for the earlier year. The Tribunal directed relief consistent with the later year's accepted treatment.

This is not an isolated result. Indian tribunals have repeatedly leaned on a consistency principle when a TPO's own later-year acceptance undercuts an earlier-year adjustment. A continuous-monitoring platform, by construction, produces the kind of multi-year, methodology-stable record that makes this argument easier to run: the more granular and continuous a taxpayer's own TP data trail becomes, the stronger its consistency-principle arguments are likely to be.

This also raises a question about asymmetry between well-resourced multinationals and the tax administration. If sophisticated taxpayers build continuous, audit-ready monitoring systems while the department still audits largely on an annual, backward-looking cycle anchored to Form 3CEB-style filings, an information and preparedness gap could open up between the two sides of the table.

India's income tax apparatus already runs AI-based risk assessment for scrutiny selection at the return level, and the CBDT's APA programme leans heavily on post-agreement monitoring of critical assumptions for bilateral agreements. From there, it is a reasonable question whether the department should build its own continuous-monitoring capability specifically for TP, to match rather than only react to the tooling that advisory firms are now selling to taxpayers.

There is also a discovery-related question worth flagging. As continuous monitoring platforms generate richer contemporaneous records than the old annual documentation model, those records could prove double-edged: they may help taxpayers win consistency arguments in years like this one, but they could also give revenue authorities a more granular trail to probe when a deviation does appear.

Neither the vendors marketing these platforms nor the tribunals applying the consistency principle have had occasion to address this tension yet. As continuous monitoring tools become more common, practitioners advising on TP documentation strategy will need to weigh the benefit of a stronger contemporaneous record against the risk that the same record gives the department more material to examine when a taxpayer's position shifts from one year to the next.

ITAT Hyderabad Extends BAPA Margin to Non-Covered AE Transactions on FAR-Identity Grounds

A Bilateral Advance Pricing Agreement negotiated with the CBDT and a foreign competent authority, usually the US given where most AE relationships sit, can cover more than 90 percent of a captive service provider's international transactions. The remainder, revenue earned from AEs in other jurisdictions such as the UK or Singapore, falls outside the agreement's scope. This residual portion must be benchmarked afresh each year and remains open to TPO scrutiny, since the certainty negotiated under the BAPA does not formally extend to it.

The ITAT Hyderabad Bench addressed this fact pattern in Synchrony International Services Private Limited v. ACIT, an order pronounced on 30 March 2026. Synchrony had a BAPA with the US covering roughly 95.75 percent of its revenue as a captive ITeS provider. The remaining 4.25 percent came from non-US AEs and sat outside the agreement. The TPO did not conduct a separate benchmarking exercise for this residual portion and proposed an adjustment on it. The Tribunal held that a BAPA margin negotiated for one country's AEs cannot be restricted to that country alone where the functions, assets and risk (FAR) profile of the non-covered AE transactions is identical, and directed the TPO to apply the BAPA rate across all three assessment years under appeal.

The outcome favours taxpayers: if a captive performs identical back-office work for a UK entity and a US entity, the pricing need not differ merely because only one entity's AE relationship is covered by the signed APA. But the ruling shifts the locus of the next dispute rather than removing it. The burden of proof on non-covered transactions is relocated, not eliminated.

Instead of a fresh comparables search each year, the taxpayer must now build and defend a case that the FAR profile across AEs is genuinely identical, covering service descriptions, decision rights, risk allocation, contractual terms, and reporting lines. This is a different exercise from a standard benchmarking study, and in some respects a harder one, because there is no external database of third-party comparables to draw on. The comparison is intra-group, AE to AE, and the evidence must come from internal documentation: service agreements, organisation charts, SLAs, cost allocation keys, and correspondence showing who directed the work.

Current AI-enabled TP tools, including benchmarking platforms that automate comparable searches and NLP tools that flag inconsistent documentation, are built mainly to address a different problem: finding and screening third-party comparables faster, or checking a local file against a jurisdiction's formatting requirements. Few, if any, are designed to assess whether AE-A's functional profile is identical to AE-B's functional profile within a single multinational group. That is a more bespoke comparability question, closer to internal audit than to database screening, and it now sits at the centre of the scope this ruling has opened.

India's APA programme has crossed 1,034 agreements since inception, with 284 bilateral, a population of taxpayers who may now have grounds to extend negotiated certainty to residual AE transactions. Doing so will require a FAR-identity case capable of withstanding scrutiny on points such as a contractual clause, headcount difference, or decision-rights nuance that could break the claim of identity.

CBDT has not yet addressed BAPA scope in this context. The Board has previously issued administrative clarifications where APA and Safe Harbour regimes interact awkwardly; the March 2026 Office Memorandum permitting taxpayers with UAPAs spanning the Safe Harbour transition to opt into the new regime for later years is a precedent for this kind of housekeeping.

A similar clarification on BAPA scope, an administrative mechanism to formally extend or fast-track non-covered AE transactions where FAR identity is not seriously disputed, could save taxpayers from re-litigating this question bench by bench, year by year, across every captive with a partial BAPA. Absent such clarification, Synchrony is likely to become a citation that captives with partial-scope BAPAs raise routinely, and one that TPOs will need to engage with on the merits rather than dismiss at the threshold.

For practitioners advising captives with partial-scope BAPAs, the practical task is to assemble FAR-identity documentation now, before the TPO raises the issue, rather than treat the Tribunal's reasoning as self-executing.

Agentic AI in Transfer Pricing: The Practical Problem Is the Handoff Between Agents

Discussion of AI and transfer pricing has largely centred on whether a single autonomous agent could take a set of intercompany agreements, run a functional analysis, select comparables and produce a defensible benchmarking range with minimal human involvement. Vendors market toward that capability, and practitioner panels debate whether such an agent could meet the reliability standards implicit in Section 92C or Section 482. A hackathon held in Vienna earlier this year, organised with the WU Tax Law Technology Center, Microsoft and TPA Global and reported only this week, points to a different pattern taking shape in practice. Rather than building one model to perform the entire task, participating teams chained together several narrower agents, each handling a bounded function.

The case studies covered intra-group financing and intercompany services: arm's length interest rates, creditworthiness assessment, the benefit test, cost allocation, method selection and documentation. Teams built separate agents for data extraction, service classification, benefit testing, cost allocation, compliance monitoring, documentation and audit readiness, and linked them into a workflow. The organisers were explicit that the intent is not to replace professional judgment: outputs are meant to remain traceable to source, reviewed before use, and subject to human oversight at each step. This combination of decomposition and human-in-the-loop review appears to be the practical model emerging from the exercise, even as vendor marketing continues to emphasise single-agent capability.

This distinction matters for Indian TP practice because the architecture of contemporaneous documentation, under Rule 10D, the erstwhile Form 3CEB and now Form 48 under the 2025 Act, assumes a single preparer's judgment trail. A TPO can ask why a particular comparable was included and expect an answer from one analyst or one firm. A pipeline of five narrow agents does not fit that assumption. If a data-extraction agent misclassifies a transaction, a downstream benefit-test agent may inherit that error and proceed regardless, since it is not designed to question upstream inputs, only to execute its own task. The more likely failure mode in a multi-agent TP workflow is not a single agent producing a wrong answer, but an error propagating silently across a handoff that no one is specifically assigned to audit. India's TP documentation requirements, safe harbour disclosures and APA application forms do not currently address this scenario; they assume one preparer whose competence and good faith can be tested under cross-examination or TPO scrutiny.

Practitioners, and possibly CBDT, will need to consider what a chain-of-custody requirement for multi-agent TP work product should look like before a dispute forces the issue. Knowing that a benchmarking output traces back to a database source, as most current vendor claims are framed, is not sufficient. It would also require knowing which agent touched the data at each stage, what it changed or flagged, and whether a human actually reviewed the boundary between two agents' work rather than only the final output. India is already working through related questions, such as how DEMPE functions performed by AI systems fit within the intangibles framework, and whether GCC functional segmentation holds up against agentic AI restructuring inside captive centres. The handoff-audit issue sits a level below those debates: it concerns not whether AI can perform a TP function defensibly, but whether anyone can reconstruct, after the fact, which of several AI agents was responsible when something went wrong. Given how current documentation standards are framed, that question is more likely to surface first in a TPO's show-cause notice than in a policy paper, and practitioners relying on multi-agent tools would do well to build their own audit trail across agent handoffs before that happens.