Technology transforms TP’s research strategy

Historically, transfer pricing (TP) advisors have relied on information gathered from external sources to perform analyzes when applying cost-effectiveness methods. Since these methods provide accurate TP analysis as more information becomes available, database providers have also relied on advances in technology to quickly and efficiently obtain information about companies and transactions made by independent third parties.

Industry classification codes used in TP practice have not been updated as market and economic conditions have changed. This is the case with the standard industrial codes issued by the US Department of Labor, which were created in 1930 and replaced by the North American Industry Classification System in 1997; that is, for 25 years these codes have not been revised or improved by TP practitioners for accurate data searches.

This becomes relevant as solid insights can be obtained through artificial intelligence and used to achieve more accurate results.

The value of data mining

Data mining has constantly evolved and become relevant to database providers and TP practitioners as they seek to generate increasingly robust information for users through technological advancement.

This affects not only searches for comparable companies via specific texts, but also information from private companies (mainly in Europe) which can now have a deeper context. Because of these developments, the use of profitability methods in public works has evolved, and will continue to evolve, by having more elements for its application.

Indeed, database providers can exploit the text of the annual reports of public companies to extract information that could be useful to their users. This is achieved through internal database interfaces and Boolean searches that work by combining concepts between one or more words to limit, expand and define search criteria.

In addition to industry codes, the main keywords used in searches for comparable companies are:

  • Functions: identify the main activities of public enterprises through concepts, whether related to manufacturing, distribution, marketing, services, etc.
  • Assets: the main resources held by companies, whether they are tangible or intangible assets.
  • Risks: in the annual reports submitted to regulatory entities, obliged entities disclose the main risks encountered during the reference period, as well as potential future contingencies.
  • Geographic regions: including the main areas or locations where the company operates.
  • Segment financial information: Relevant to TP analysis as it presents results by divisions, product types or regions of the world.

This keyword research is not limited to finding comparable companies. It is also possible to obtain agreements between independent third parties, whether for services, licenses of intangible assets or financing.

Indeed, service providers can identify the content of the clauses of the agreements signed on the market. Similarly, some of the terms that can be used to search for comparable contracts are:

  • Dates of signature and expiry: to define the period during which the operations remained in force, as well as the modifications of the date of conclusion;
  • Parties involved: includes the trade names of the signatory entities, as well as certain others that may be affected under the contract (for example, guarantees);
  • Geographic scope: as in the previous paragraphs, it defines the regions where the operations are carried out;
  • Intangible or service of the contract: the subject of the agreement indicates whether the parties have agreed on a service to be provided, financing granted, etc. ;
  • Remuneration basis: this data is crucial because there are different ways of agreeing remuneration (sales, costs, expenses, assets, profits, etc.);
  • Exclusivity: determines whether the agreements to be analyzed include exclusivity clauses between the parties; and
  • Relationship: Data mining can determine if the agreement includes related parties.

Business-to-business analytics

Given advances in artificial intelligence for data mining – generating accurate and robust insights into transactions or comparable companies – TP firms in emerging markets have the opportunity to conduct cross-company analysis. These increasingly meet the comparability criteria set out in the OECD Transfer Pricing Guidelines when considering the economic and commercial circumstances applicable to the reality of each case.

Finally, it is important to keep in mind the specific and high technical requirements set by the tax authorities. In particular, emphasis should be placed on using the deductive approach provided for in the OECD Transfer Pricing Guidelines when preparing searches for transactions or comparable companies based on keywords. . This stipulates that the procedure for finding comparable companies or operations generally begins with a wide range of potential comparables, to be refined by quantitative and qualitative criteria.

Artificial intelligence responds to these claims by eliminating bias, starting from a broad base of potential comparables, and making research processes transparent and accurate. It considers not only in general terms the functions, assets and risks, but the specific economic and business circumstances to increase the criteria for comparability.

Dan Paul Hernandez de Aguirre

Head, HLB MAAT Asesores

Oscar Antonio Salinas Lopez

Manager, HLB MAAT Asesores

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Brandon D. James