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.

TR 2026/2: Australia's Software Royalty Ruling and Its Implications for Groups with Indian Operations

On 4 September 2026, the Australian Taxation Office finalised Taxation Ruling TR 2026/2, along with a draft Practical Compliance Guideline, setting out its position on when cross-border payments for software, SaaS access, and related IP arrangements amount to 'royalties' subject to withholding tax. The ruling took five years to finalise, replacing a position that traces back to the withdrawal of an older ruling in 2021. It follows the Australian High Court's decision in PepsiCo, which the ATO has relied on to justify a substance-over-form, purpose-focused test for what counts as a royalty.

The fact pattern is close to one India's Supreme Court resolved in 2021, in Engineering Analysis Centre of Excellence v CIT. That decision ended nearly two decades of litigation by holding that payments to non-resident software suppliers for resale or use under distribution agreements and end-user licences are not royalty payments, because what is transferred is a copyrighted article, not an interest in the copyright itself. India adopted the narrow, taxpayer-favourable reading. TR 2026/2 goes the other way, and further: it takes the position that where IP rights are practically inseparable from the other commercial rights in a software intermediation arrangement, which the ATO treats as the common case, the entire payment is characterised as a royalty, with no workable apportionment pathway in the final text. The draft compliance guideline layered on top classifies most royalty-free distribution structures as medium-to-high risk unless the local margin clears a threshold meaningfully higher than the ATO's own prior benchmark for low-risk distributor margins.

The commercial reality of how software gets distributed cross-border has not changed. What has changed is the doctrinal lens two major tax administrations apply to that reality, and the two now point in opposite directions. This matters because many multinational software distribution structures are built on a common template rolled out across several jurisdictions at once. A group that priced its Indian and Australian distribution entities on the same functional analysis and the same intercompany agreement now faces two different downstream outcomes. In India, the royalty question was settled years ago, so the TP analysis stands on its own. In Australia, the royalty characterisation question sits ahead of the TP question and can override it: if the whole payment is a royalty, the arm's length distribution margin analysis becomes secondary to a withholding tax exposure that was never priced into the original structure. MinterEllison's analysis of the final ruling described this as a 'full-royalty rather than apportioned outcome' as the default assumption, a marked hardening from where the draft guidance started.

Whether this is an Australian idiosyncrasy or an early sign of a broader trend is not yet clear. Some revenue authorities, facing fiscal pressure, have shown greater willingness to use substance-based, purpose-focused tests to recharacterise payments, an interpretive approach not unlike the one behind India's GAAR and its expanded commercial substance doctrines. If more jurisdictions begin reading embedded software royalties this expansively, Indian multinationals licensing software into distribution networks abroad, and Indian subsidiaries of global software groups receiving payments from Indian customers, may need a characterisation review ahead of every TP benchmarking exercise, rather than a periodic refresh of comparables alone. For now, groups with common distribution documentation across India and Australia should map where the two jurisdictions' positions on the same contract diverge, rather than assume that a settled Indian royalty position carries over wherever the same paperwork is used.

When the Service Provider Is an Algorithm: What Chapter VII's AI Problem Means for Indian Captives

A captive Indian entity, such as a GCC, a KPO, or a testing centre like the one at the heart of the Honda R&D India ruling this month, performs a defined function for its overseas parent. The transfer pricing analysis asks three questions: whether a service was rendered, whether the recipient derived a benefit, and what cost-plus markup reflects an arm's length charge for that service.

This is the architecture of Chapter VII of the OECD Transfer Pricing Guidelines, and it underlies most of India's Safe Harbour Rules, most APAs for ITES/BPO structures, and a large share of the ITAT docket. It works cleanly as long as the item being delivered is recognisably a service: bounded, routine, and separable from any underlying intangible.

In June 2026, the OECD's Working Party 6 released a discussion draft proposing a substantive rewrite of Chapter VII, including a new accurate-delineation analysis, an expanded benefit test, a sharper shareholder/stewardship distinction, and new guidance on the boundary between a service and an intangible. Comments closed on 22 July, and the OECD published the full set of responses on 24 August: more than one hundred submissions from businesses, industry bodies, and advisory firms.

A recurring theme across those submissions was a call for clearer distinctions between intra-group services and intangible transfers, particularly for AI-enabled and digital service models. This suggests that practitioners find the existing framework difficult to apply to AI-delivered outputs, a chapter otherwise treated as settled doctrine since the 1990s.

The captive/GCC model is central to India's transfer pricing practice, not a peripheral segment of it. Thousands of Indian entities are remunerated on a cost-plus basis for the kind of routine, human-performed functions that Chapter VII was built to price: testing, market research, back-office processing, and KPO analytics. Many of these entities are, in 2026, incorporating agentic AI into the delivery of these functions, which changes what sits inside the service line item rather than replacing it.

If a GCC's cost-plus-remunerated data analytics service is increasingly produced by an AI agent trained on group data, proprietary models, or a parent's algorithms, the question is whether the Indian entity is still delivering a 'service' in the Chapter VII sense, or something closer to an output derived from an intangible it does not own. That distinction could imply a different pricing method, a different DEMPE analysis, and possibly disqualification from the Safe Harbour margins (now consolidated into the unified 15.5% IT Services band) that assume a routine, low-risk service function.

Two other developments this week bear on the same issue. Turkey's Tax Inspection Board launched an AI system in August built specifically to flag transfer pricing risk in related-party transactions, which suggests some revenue authorities are building AI capability aimed at this kind of transaction. On the advisory side, Nexdigm's alliance with the AI-native TP platform infer360 shows Indian mid-tier firms building AI-TP capability of their own, partly in anticipation of this characterisation issue becoming a live audit question rather than an academic one.

Whether the CBDT should clarify, before the 2027 filing season, whether AI-enabled service delivery inside a GCC/ITES structure remains within the Safe Harbour and cost-plus framework, or whether it needs a separate carve-out, is a question worth raising now. The Chapter VII revision will not be finalised until after the November 2026 Paris consultation, so India has a window to shape its own domestic position rather than import whatever the OECD eventually adopts.

Given how much of India's outbound service economy sits on this fault line, practitioners advising GCC and ITES clients would do well to flag the characterisation risk in current TP documentation, ahead of any assessment order that forces the issue.

Post-SAP Labs Rulings Tighten Scrutiny of Comparability Filters in TP Benchmarking

The Karnataka High Court has disposed of a batch of transfer pricing appeals that had been pending since the Supreme Court's 2023 judgment in SAP Labs sent them back for fresh consideration. The appeals turned on a familiar but consequential question: how much latitude a High Court has to examine the comparability filters, such as turnover bands, related-party-transaction (RPT) thresholds and export-ratio cutoffs, that determine which companies are shortlisted for TNMM or CPM benchmarking.

Under the Karnataka High Court's earlier Softbrands precedent, comparability disputes, including which companies get excluded and which filters apply, were treated as pure questions of fact on which a High Court would not ordinarily interfere. SAP Labs removed that immunity. In this month's ruling, the Court held that it can examine whether the selection of comparables and the choice of filters was made judiciously and on the basis of relevant material, though it will continue to defer to the Tribunal unless the process is shown to violate Section 92C or Rule 10B, or is otherwise perverse. On the facts, the Court upheld a Rs 200 crore upper turnover filter as rational given the size, brand value and economies of scale of the tested party, and held that a 15% RPT filter is ordinarily appropriate. A higher threshold of 20% or 25% is permissible only where the TPO records a specific finding explaining why comparables meeting the lower threshold were unavailable. The Court also held that the tolerance band of plus or minus 5% under Section 92C is not an automatic standard deduction from the arithmetic mean; it is a threshold that determines when an adjustment is required, not a discount to be applied as a matter of course.

In the same week, the Delhi ITAT reached a comparable conclusion in GE India Industrial's case. The Tribunal rejected the TPO's rigid 50% turnover filter and held that functionally comparable companies cannot be excluded on size grounds alone; comparability must be tested against the functions, assets and risks framework under Rule 10B, not against an arbitrary turnover band. Read together, the two rulings direct TPOs and taxpayers to support each filter with a documented, fact-specific rationale tied to the taxpayer's actual FAR profile, rather than applying it as a default setting.

This has a direct bearing on how benchmarking is now conducted. Commercial and in-house benchmarking tools typically ship with default screening logic: an RPT filter set at 15% or 25%, a turnover band expressed as a multiple of the tested party's revenue, an export-ratio cutoff for captive units. Agentic benchmarking tools, which some practices have begun piloting, go further: they select the filter to apply, often based on the vendor's training data or built-in heuristics, rather than on a documented judgment call by the practitioner running the analysis.

Read against the Karnataka High Court's ruling, the choice of filter thresholds, not merely the choice of method, is now a part of the TP file that could attract closer scrutiny. A TPO or a taxpayer's advisor who cannot explain why a tool applied a 25% RPT filter instead of 15%, beyond the fact that this is the platform's default, may find that position harder to sustain in a perversity challenge than it would have been before SAP Labs.

This raises a practical question for practitioners advising on AI adoption in benchmarking. Building an auditable, tool-agnostic justification for each filter choice adds documentation overhead, but it may also be the record that a TPO or Tribunal now expects to see. Conversely, the push toward faster, high-volume automated benchmarking runs could encourage greater reliance on unexamined defaults at the same time as the case law raises the cost of doing so. Industry commentary describing 2026 as the year in which touchless compliance becomes a practical necessity has largely framed that shift around GloBE Information Return and Form 6765 deadlines rather than TP substance, which suggests that current AI-in-tax roadmaps are built primarily for data processing rather than for the judgment-intensive task of justifying filter choices that these rulings have placed back on the table.

For practitioners, the immediate implication is procedural rather than strategic: any benchmarking filter applied by a tool, whether commercial or agentic, should be accompanied by a documented rationale linking the threshold to the taxpayer's FAR profile, so that the choice can be defended as a reasoned exercise rather than a software default if it is questioned.

Agentic AI in Transfer Pricing Benchmarking: What a New Study's Error Rates Mean for Indian Practice

A study published in The International Tax Journal tests, with data rather than assertion, a claim commonly made for AI-driven transfer pricing benchmarking tools: that autonomous systems can produce faster analysis without a corresponding loss of rigour. The findings deserve closer attention from Indian practitioners than they are likely to receive.

The authors ran agentic AI, systems that execute a multi-step workflow such as a benchmarking search or functional analysis without step-by-step human prompting, against 20 simulated MNE cases across three sectors. They compared the outputs against a model of how tax administrations in 11 countries are deploying similar technology. The efficiency gain was substantial: agentic AI cut processing time by roughly 98% while increasing the depth of the functional analyses produced.

The same study reports three failure rates that merit attention before any TP head signs off on an AI-assisted benchmarking pipeline: a 15% error rate in complex functional characterisations, a 22% irrelevance rate in comparable selection, and a 15% hallucination rate in legal citations. The authors propose a governance framework they call "Tracer-Wire," under which every AI-generated conclusion must carry a visible, auditable path back to its source data, with a mandatory human checkpoint before any output is finalised.

Explainability-by-design and human-in-the-loop review are now standard features of AI governance proposals, so the framework itself is not the notable part of the paper. What is notable is the coincidence between the study's error rates and recent Indian tribunal outcomes.

This month alone, three decisions have turned on the same issue the study measures. The Delhi bench of the ITAT excluded two comparables from Dixon Technologies' set for functional dissimilarity, even though the taxpayer had itself flagged the issue years earlier. The Chennai bench devoted an entire order to whether a single internal comparable can still claim the statutory tolerance band. The Karnataka High Court's SAP Labs line of rulings has produced a further set of follow-on decisions this month on whether a TPO may discard a taxpayer's comparables in favour of a "standard set."

Each of these disputes falls within the 22% irrelevance-rate failure mode the study measured. If agentic AI misjudges comparable relevance roughly one time in five even in a controlled simulation, and Indian tribunals are already spending full orders correcting comparable-selection errors made by humans, the practical question for TP practice in 2026 is concrete rather than conceptual: whose signature appears on the local file when an AI-selected comparable turns out, on review by a TPO or an ITAT bench two years later, to be a functionally dissimilar entity such as a plastics manufacturer.

Coverage of AI in transfer pricing tends to avoid a question that is uncomfortable for vendors and practitioners alike: whether an audit trail reduces liability or merely relocates it. A Tracer-Wire log showing that an AI system considered and rejected a comparable for a documented reason does not make that rejection correct. It makes the error more visible, and it arguably shifts accountability toward whoever approved the workflow rather than toward the tool itself.

It is worth asking whether India's Master File and Form 3CEB documentation requirements are structured to capture this kind of AI-decision provenance at all. If they are not, practitioners may be looking at a new category of documentation gap in an area the OECD's Chapter V framework was never designed to address. Firms adopting agentic AI for benchmarking would do well to build a sign-off protocol now, one that fixes responsibility for each accepted comparable before a tribunal does it for them.

DEMPE's Human-Centric Assumptions Face a Test as AI Performs Development and Enhancement Functions

The DEMPE framework determines which group entity is entitled to the return on an intangible by examining which entity performed the Development, Enhancement, Maintenance, Protection and Exploitation functions, made the key decisions, bore the risk, and had the people and capability to control what was happening. The analytical apparatus, functional interviews, organisation charts, decision logs, and the contemporaneous email trail showing an engineer approving a design change, rests on an assumption that a human being performed the function. A recent piece of Australian tax commentary notes that this assumption is becoming harder to sustain. The Australian Taxation Office's guidance on intangibles migration, PCG 2024/1, sets out the evidence it expects a multinational to produce when documenting its DEMPE functions. That evidence regime, the commentary observes, presumes human decision-making and physical performance. It does not address what happens when the decision is made by a model.

PCG 2024/1 has been in force since January 2024. The law itself has not changed; what has changed is a growing recognition that the gap between the rule and the operating reality is becoming practically significant. AI systems are themselves valuable intangible assets under ordinary transfer pricing principles: the algorithms, the training data, and the fine-tuned models sitting inside a captive centre's codebase. A separate issue arises one level up. When an AI system performs the actual development and enhancement work, writing and testing code, running design iterations, flagging defects, optimising a process, the question is who counts as the DEMPE contributor. It could be the engineers who built and supervise the model, the entity hosting the compute, the parent that trained the underlying model elsewhere, or no clearly identifiable party if performance becomes diffuse. The commentary frames this as a mismatch between a human-centric international tax architecture and an operating reality that is automating functions the architecture was built to track.

The implications are likely to be more pressing for India than for Australia. India's transfer pricing landscape relies heavily on captive centres and GCCs performing the kind of granular, iterative technical work, software development, testing, engineering support, and increasingly research, that is most amenable to AI augmentation and, potentially, AI substitution. For two decades, the Indian TP dispute over these centres has centred on characterisation: whether a centre is a routine cost-plus service provider or a genuine value-creating R&D contributor, and which comparables support either position. That analysis has assumed the underlying question, who did the work, could be answered by examining headcount, job descriptions, and reporting lines. If an increasing share of the development and enhancement work inside these centres is performed by AI tools that the centre operates but did not necessarily build, the functional analysis becomes harder to write convincingly in either direction. A TPO could argue that the Indian entity performs less genuine DEMPE work than its cost base suggests, because the model does the underlying processing. A taxpayer could equally argue that the Indian entity deserves more than a routine cost-plus return, because supervising, curating, and directing an AI system that performs high-value technical work is itself a sophisticated function that current benchmarking studies are not equipped to price.

The functional analysis may need a distinct category, one that asks not only who performed the function but who exercised meaningful control and judgment over the system performing it. Whether existing documentation templates, functional interviews, organisation charts, decision logs, can capture that distinction without substantial revision is unclear.

A related institutional question follows. If DEMPE evidentiary standards assume human decision-makers, and the CBDT's own scrutiny and risk-selection processes are moving toward AI-driven flagging at the same time, the tax administration sits on both sides of the same conceptual gap: applying a human-centric framework through tools that are themselves not human-centric. For practitioners, the practical implication is to begin documenting, now, the extent of human supervision, curation, and judgment exercised over AI tools used in development and enhancement work inside Indian captive centres, since existing DEMPE templates may not capture that distinction without adaptation.

Aggregate or Segregate: Why the Choice Cannot Be a Default Setting in AI Benchmarking Tools

Transfer pricing benchmarking requires an early decision on whether to test a transaction on a standalone basis or aggregate it with other international transactions of the entity. Rule 10A's language on aggregation can support either approach depending on the facts, and taxpayers and tax authorities routinely take opposing positions on which approach the facts justify. A recent ITAT Mumbai ruling in the case of NTT India (formerly Dimension Data India) addressed this question directly, and its reasoning has implications for how AI-assisted benchmarking tools are being designed and used by Indian tax teams.

NTT India had benchmarked its management-fee payment to its Asian regional AE as part of an aggregate, entity-level TNMM analysis, arguing that the overall margin was at arm's length once all international transactions were considered together. The TPO disagreed, extracted the management-fee transaction from that aggregate analysis, applied the CUP method, found no comparable uncontrolled data, and valued the entire service at nil, resulting in an adjustment of nearly ₹93.23 crore. The ITAT deleted the adjustment. In November 2025, a separate ITAT Mumbai bench reached a similar conclusion in an unrelated case, holding that a TPO cannot accept TNMM for a taxpayer's transactions in aggregate and then isolate a single line item for independent nil valuation. The two rulings, from different benches and about ten months apart, apply the same underlying principle.

Rule 10A's aggregation language has not changed. What appears to have shifted is how often tribunals are being asked to police where the aggregation boundary sits, and the consistency with which they are ruling against the department on this point. For a TP practitioner, this strengthens the argument that once an aggregate TNMM position has been accepted, or at least not affirmatively rejected, for an assessee's transactions as a whole, the TPO's room to isolate and independently value a single line item is narrower than it may once have appeared.

This reasoning is also relevant to AI-assisted benchmarking and documentation tools, including agentic platforms and GenAI-based comparable-search products, that are being marketed to Indian tax teams and Big Four practices. Most of these tools make an implicit aggregation-or-segregation choice somewhere in their workflow. Some default to pulling entity-level financials and running margin comparisons across the whole profit and loss account. Others are built to isolate and test each intercompany transaction separately because that is easier to automate and audit.

Neither default is safe on its own. A tool that always aggregates risks reproducing the outcome favourable to the taxpayer in NTT India even in fact patterns where aggregation is not actually justified, inviting a TPO challenge on the opposite theory. A tool that always segregates transactions for cleaner, auditable output risks reproducing the same TPO error that was overturned twice within about a year, testing a management fee, a cost-contribution arrangement, or an IT service fee in isolation when it was never meant to be tested that way. The tribunals' reasoning indicates that this decision has to rest on how closely the transactions are linked on the specific facts, not on a default setting built into a product.

This has a direct implication for how TP teams evaluate any AI benchmarking tool they consider buying or building. Vendors are likely to emphasise comparable-search speed and documentation drafting, but the more relevant question for audit defensibility is narrower: does the tool make its aggregation-or-segregation choice explicit, does it require a person to record the specific factual basis for that choice, and would that basis survive a TPO challenge along the lines the TPO raised in NTT India.

As more Indian captives and GCCs adopt AI-assisted TP documentation workflows, practitioners should treat the aggregation-or-segregation call as a documented, fact-specific judgment that sits with a person on the team, not a default a vendor sets. The NTT India line of rulings gives that judgment more weight than it may have carried before, and it is a reasonable basis for reviewing any AI-generated benchmarking file before it is relied upon in a submission to the TPO or the DRP.