Valuation of Intangible Assets in India — Brands, Customer Relationships, and Technology under Ind AS 38 and Ind AS 103

The valuation of intangible assets — brands, customer relationships, technology platforms, intellectual property, non-compete agreements, and distribution networks — is one of the fastest-growing areas of professional valuation practice in India, driven by the intersection of three forces: increased M&A activity requiring Purchase Price Allocation under Ind AS 103, the growing sophistication of financial reporting auditors who no longer accept unsupported goodwill as a residual, and the increasing recognition by Mumbai’s private equity and strategic investment community that intangibles often represent the majority of the economic value they are acquiring. Yet the technical standard of intangible asset valuation practice in India lags significantly behind the level routinely delivered by Big 4 valuation teams in London, New York, or Singapore — and that gap represents both a professional risk for companies that rely on inadequate valuations, and a market opportunity for practitioners who can genuinely close it.

The foundational framework for intangible asset valuation is the income approach, specifically the multi-period excess earnings method for primary intangibles and the relief-from-royalty method for technology and brand assets. The MPEEM, described earlier in the context of PPA, is also the appropriate method for valuing customer relationships, subscriber bases, patient or client books, and other revenue-generating contractual relationships in standalone contexts — for example, when an NBFC is acquiring a customer portfolio from a distressed lender, or when a financial services group is separately valuing its customer relationship asset for impairment testing purposes under Ind AS 36. The MPEEM requires a detailed cash flow model that disaggregates the total entity value into contributions from each asset class — and the valuer must make explicit, defensible choices about the contributory asset charges applied to tangible assets, working capital, assembled workforce, and secondary intangibles. These choices directly affect the value attributed to the primary intangible, and they are the source of the most frequent methodological disagreements between valuation advisors and auditors in Big 4-reviewed transactions.

MPEEM and Relief From Royalty — The Two Methods That Drive Intangible Asset Valuation in Indian M&A

The Relief from Royalty Method is the dominant approach for brand and technology valuation. It is conceptually elegant — the value of the intangible equals the present value of the royalty payments that the owner avoids by owning the asset rather than licensing it from a third party. The implementation requires three inputs: a royalty rate, a revenue base to which the rate is applied, and a discount rate reflecting the risk of the royalty cash flows. The royalty rate is derived from market evidence — observable licencing transactions in comparable industries, reported royalty databases (such as RoyaltyStat or ktMINE), and the asset’s contribution to profitability. For Indian brand and technology assets, the relevant royalty rate evidence is often drawn from a combination of Indian licencing transactions — which are fewer and less well-documented than in developed markets — and international comparables adjusted for the Indian market context. The adjustment methodology is a matter of professional judgment, and its documentation is where the analytical rigour either holds together or falls apart under auditor challenge.

For financial services brands in Mumbai — including NBFC brands, insurance brands, and asset management brands — the brand valuation exercise is particularly nuanced. Financial services customers choose providers based on trust, institutional stability, pricing, and distribution accessibility. Isolating the brand’s independent contribution to revenue — as distinct from the distribution network, the pricing power, and the regulatory licence that the institution also possesses — requires a careful analysis of consumer research, premium pricing data, and comparative performance against generic or unbranded equivalents. Where comparable data is available — for example, through RBI deposit rate data, insurance premium comparisons, or mutual fund AUM growth analysis — the brand premium can be estimated with reasonable precision. Where it is not, qualitative scoring methods calibrated to market evidence are the appropriate fallback, with explicit documentation of the scoring criteria and their weights.

For technology platform valuation in Mumbai’s active fintech, insurtech, and wealthtech M&A market, the relief-from-royalty approach must be supplemented by an assessment of the technology’s competitive moat — the degree to which the platform provides sustainable competitive advantage that cannot be replicated by a market entrant within the asset’s expected useful life. Technology that is commercially deployed but easily replicable by a well-funded competitor within two to three years has a significantly different value profile than a platform that took five years and significant capital to build, is deeply embedded in client workflows, and is protected by regulatory barriers or network effects. The valuer’s assessment of competitive moat directly affects the useful life assumption, which is a primary driver of the Relief from Royalty value. For Indian technology assets, where the pace of technological change is rapid but the cost and regulatory barriers to fintech deployment are also significant, the useful life assessment requires both technical literacy and market knowledge that generalist valuers do not always bring to the engagement.

The practical challenge of obtaining reliable royalty rate evidence for Indian brand and technology assets deserves more focused treatment than it typically receives in valuation practice. The Relief from Royalty methodology is theoretically clean, but its application requires royalty rates that are relevant to Indian market conditions — and the published royalty databases that practitioners use, including RoyaltyStat and the Licensing Economics Review, are heavily weighted toward transactions in developed markets, primarily the United States. Applying a US technology royalty rate of 8-12% to an Indian technology platform without adjustment implicitly assumes that the licensing economics for Indian technology are equivalent to those in the US market — an assumption that is difficult to defend when the Indian software market commands materially lower pricing for equivalent functionality than US equivalents.

The adjustments required to bring international royalty rate evidence to the Indian context typically include a downward adjustment for the lower technology pricing environment, an upward adjustment for the scarcity of comparable licensed alternatives in the domestic market (which may command a premium), and an adjustment for the functional differences between the subject technology and the comparable licensed assets in the database. Each of these adjustments requires judgment and documentation — and the combination of adjustments can produce a royalty rate range that is both directionally defensible and wider than practitioners are comfortable presenting. The right response to this uncertainty is not to narrow the range artificially but to perform sensitivity analysis on the royalty rate assumption and to present the full range of values that result from reasonable rate variation.

For India’s fast-growing new-economy sectors — fintech, insurtech, edtech, and B2B SaaS — the intangible asset landscape is particularly complex because the competitive moat often lies not in a single identifiable technology platform but in the combination of technology, data assets, network effects, and regulatory relationships that constitute the business’s competitive position. Separating these into individually identifiable and separately measurable intangibles — as Ind AS 103 requires for PPA — involves analytical decomposition that does not always map cleanly onto how the business actually generates value. The Multi-Period Excess Earnings Method, applied carefully, provides the most principled approach to this decomposition because it explicitly models the contribution of each asset class to the total cash flow stream, and the residual attribution after all contributing assets have been charged represents the intangible being valued.

Our valuation practice at Harshul Mangal & Associates includes Purchase Price Allocation and intangible asset valuation for M&A transactions under Ind AS 103, conducted under the IBBI Registered Valuer framework (Reg. No. IBBI/RV/16/2025/16044) with methodology that meets the professional standards required by Big 4 audit teams reviewing business combination accounting.

For further reading on the regulatory framework governing this area, refer to the ICAI guidance on Ind AS 38 — Intangible Assets, which provides the primary regulatory foundation for the analysis discussed here.

Our valuation services cover the full range of SFA assignments described in this post — from regulatory compliance to transaction support. If you need professional valuation assistance, we would be pleased to assist. You can reach out to us here or write to harshulmangal.ca@gmail.com.