• Andreas Wiesner, Director |
  • Christian Krämer, Expert |

The compilation of financial data and ratios for TPD and quality checks to corresponding topics pose multiple challenges every year. Technology and clearly defined rules and logics can fully automate the process. This is accompanied by increased data quality and traceability of the processed data.

Introduction – Challenges in common practice

Multinational groups are faced with increasing compliance requirements regarding transfer pricing documentation (“TPD”) in many jurisdictions. In connection with our blog Practical automation of IC transactions for TPD (see below in the links), the preparation of the financials and profit level indicators ("PLI") are a very time-consuming, recurring and sometimes very tedious endeavor. Also, it is not uncommon in this regard that several data sources and files are used so as to get to the desired result, and with every manual step there is a risk of unintentional errors. For people not involved, it is often difficult to fully understand and track how the financials in TPD were compiled. Sometimes it is even done inconsistently from year to year even for the same entity. When the multitude of spreadsheets used or the sometimes complex formulas crash or the responsible staff members change, this is when problems become overwhelming.

Five practical examples of where the automation of financial data preparation and analytics makes sense

As in the case of intercompany transactions, automating tedious and rather routine tasks such as the preparation of financials for TPD is very helpful, especially as this is a sensitive issue where quality and consistency are often paramount. Data analytics topics go hand in hand with this. Here are five practical examples:

  1.  Compilation of the P&L and/or balance sheet financials according to the required TPD structure using clearly defined rules to ensure the highest quality and consistency. This can be done, for example, by adding a relevant natural account mapping, which also takes into account the different subtleties of the various countries and regions. The calculation of different PLIs can also be automated in the same step.
  2. Automatically check the PLIs against the underlying benchmarking studies to have a quick management view of where there might be points of discussion.
  3. Automatic and rule-based preparation of segmented financials. By using clearly defined key criteria, the quality and consistency of the function-specific PLIs, which can be tested with a benchmarking study, can be improved and calculated more accurately. This is also very helpful as a purely internal financial management tool for strategic decisions.
  4. For TPD purposes, financials are often scrutinized only at the end of the year or thereafter. Using a clearly defined data model, periodic information can be provided during the year on whether the respective group entity is within the target margin or the benchmark range, ideally by function through segmented financials. With the help of end-to-end automation of pricing, margin monitoring and reconciliation processes, better operational transfer price decisions can be made and any year-end adjustments be prevented.
  5. In connection with the automation of intercompany transactions, the verification of the applied mark-ups in connection with the achieved margin must also be analyzed. Depending on data quality data and availability, this may provide more precise information about the origin of any deviations.

Beyond automation, visualisation can help data tell a story that contributes to a better understanding of the financials and corresponding topics, as well as to an enhanced decision-making in strategic planning.


The typical benefits of the automation of financials and the other practical examples are:

  1. Process acceleration: automate and speed up iterative processes and calculations as well as generate a clear mapping
  2. Efficiency increase: automate time-consuming, manual data tasks and adjustments
  3. Reduction of errors: achieve a higher data quality by implementing repeatable data quality checks including an overview of findings
  4. Increase transparency: achieve a high level of transparency of processes, applied rules and logics as well as full traceability of processed data
  5. Insights: spend less time processing data and more time analyzing it for strategic business decisions with intuitive user interface

As soon as the data model has been set up, high efficiency gains can be realized, especially in the following years. Typically, only the raw data of the new documentation cycle needs to be added, and the data mapping confirmed (and if necessary updated). The processing of the data is then fully automated according to the defined rules and logics, and the desired data is available in next to no time.

Get started with your own case

In order to start, you need to identify the required input data. Secondly, elaborate the steps to get from input to the desired output required for TPD. And finally, define clear rules and logics that can be applied repetitively year by year for an automated financial data processing and further analytics and visualization of it.

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