Many treasury experts, tax professionals and tax authorities consider the CUP method best practice for most types of intercompany loans. However, discussions arise on how to apply the method. It is very common that different arm’s length prices are concluded using the same method as different approaches lead to different comparables. This article illustrates the common pitfalls when applying the CUP method for loans while suggesting a best-practice approach to selecting comparables and determining the appropriate comparability adjustments.
Objectifying the CUP analysis through risk-based pricing
There is often a common understanding on which loan characteristics drive the pricing of a loan: the credit rating, maturity, securization and repayment schedule. Selecting the most comparable transactions based on these criteria is subject to a process. Unfortunately, many practitioners will use arbitrary filters to narrow down the data set. For example, one may decide to filter on the resp. credit rating and the adjacent notches while another may filter only on the resp. rating. This may skew the results and lead to cherry picking.
Zanders proposes to select comparables in an objective manner. Banks price loans based on their aggregate credit risk profile. This is a measure put forward by the Basel guidelines and combines all relevant credit risk drivers set forth by the OECD guidelines. It gives a unique basis to compare any intercompany loan to the entire universe of comparables.
Graph 1: sample regression to illustrate the the risk-based pricing
Those comparables that require the least comparability adjustments to arrive at the aggregate credit risk profile of the intercompany loan are most comparable. Using this methodology, corporates eliminate the risk out of the CUP method. This will lead to significant time savings when audited. Additionally, the methodology is ideal to automate making the CUP method for intercompany loans a routine process.
Market data: sources, timing and data validation
Both taxpayers as well as tax authorities rely on the same market data providers. This eases transfer pricing audits. However, small differences may exist between different market data providers. Therefore, it is important to document appropriately. It is wise to store unique references towards market transactions that are identical across providers. For example, include the ISIN of each comparable in the transfer pricing documentation. Different timing may be another source of dissimilarities. It is therefore also recommended to save the retrieval date of the market data, next to the unique reference.
Certain types of data require a validation process. For example, when using bonds as comparables it would be wise to check for data completeness and correctness. The validation rules, as well as the various market data providers, are a standard section in each best-practice transfer pricing policy. It may also explain why a certain ISIN is not present in the data set. This will allow the corporate to have a defendable data set.
Transparency is key when applying economic models
The OECD points towards the importance of transparency when using economic models to determine arm’s length interest rates. It is not enough to only document the data sources and validation rules in the policy. Transaction-specific reports should also include the selected comparables and their resp. comparability adjustments. The combination of the policy (which includes the methodology and data sources) in combination with the transaction-specific report should enable tax authorities to replicate your process. Automation on both the pricing and documentation processes are key for any lean treasury and tax department. More information on this topic can be found it our related article: Compliant documentation made simple.
The CUP method is widely used when pricing intercompany loans. However, disputes arise when the selection of comparables and the calculation of comparability adjustments follow different processes. Eliminate the transfer pricing risks by using the aggregate credit risk profile as an objective basis to compare the riskiness of various financial transactions. In combination with adequate documentation of market data processes and a transparent TP analysis, it should enable tax authorities to easily understand and replicate your transfer pricing study.