A recent Mumbai ITAT ruling in a captive power transfer pricing dispute turned on a question that AI benchmarking tools are not built to ask: whether the comparable a taxpayer needed was already sitting inside its own books.
The case involved a cement manufacturer whose captive power plants supplied electricity to its own cement units. The dispute arose under the 2013 amendment that brought specified domestic transactions within the arm's length pricing framework. The Revenue argued that the captive generator had to be benchmarked against the rate at which other generators sell to state distribution licensees. The taxpayer argued that its own cement units paid documented market rates to state power distribution companies for electricity purchased on the open market, and that this internal rate was a valid comparable for what the captive plant charged them. The tribunal agreed. It held that the amendment does not require a generator to be benchmarked only against another generator, or that a distribution licensee's procurement rate must invariably be adopted, and that CUP selection remains governed by the transaction-specific requirements of section 92C and Rule 10B. Revenue's appeals against adjustments of roughly Rs. 64 crore and Rs. 43 crore across two years were dismissed.
The outcome is less significant than the source of the winning comparable. It was not retrieved from a database of unrelated third-party companies, the kind of external, industry-classified, margin-screened universe that commercial TP benchmarking tools are built to search. It was already in the taxpayer's own transaction records: the price its own units paid the grid for the same commodity, in the same period, in the same geography. An external comparables search, however well it ranks functional similarity, would not have surfaced this fact pattern, because the relevant exercise was not searching further afield but looking at what the group itself was already paying for the identical input elsewhere in its operations.
This points to a gap in how AI benchmarking tools are currently designed. Internal CUPs sit at the top of the comparability hierarchy because they avoid much of the functional-comparability subjectivity that external searches have to approximate statistically. Yet most AI tooling marketed to TP teams is built to screen external databases faster, not to mine a group's own intercompany and third-party transaction data for an internal benchmark. These tools automate work that used to be slow, external database screening, but they are not generally designed to surface the kind of internal comparable a case like this one turned on.
A separate observation from this filing season's TP engagements reinforces the point. Some practitioners report that adjustments are more often lost on evidence discipline than on legal interpretation: the comparable search cannot be reproduced, or the Local File and the counterparty's file tell different functional stories. Reproducibility of an external search is a baseline requirement. It says nothing about whether a better internal comparable was considered at all.
For TP practitioners evaluating AI benchmarking tools, this ruling is a useful prompt to ask a specific question: does the tool query the group's own ERP and intercompany transaction history for internal comparables before it touches an external database, or does it only speed up external screening under a new label. Before commissioning an external search, it is worth checking whether the answer was already in the ledger.
No comments:
Post a Comment