The compilation of intercompany transaction data for TP documentation poses a multi-faceted challenge 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. A very time-consuming, recurring and sometimes very tedious task for TPD is the compiling of intercompany transactions. It is not uncommon that several data sources and files are used in order to get to the desired result. Especially in the case of manual compilation, it can happen that errors slip in and data is processed inconsistently compared to the previous year. It is often difficult to fully understand how the transactions were compiled and sometimes inconsistencies crop up between countries and regions. If the huge amount of spreadsheets used or partially complex formulas crash, or the responsible staff members change, the problem is in full swing.
Automating tedious and rather routine tasks therefore makes more than sense in order to devote valuable time to more complex issues within transfer pricing. Our Tax Function Benchmarking Survey, which surveyed a large number of Swiss companies, found that time / resource constraints are a big challenge and that the biggest opportunities for streamlining are within Transfer Pricing.
Automation: Key aspects to consider
An important prerequisite for fully leveraging the capabilities of automation is having a clear understanding of the current and target processes. This includes a full understanding of how and where data is currently stored. Is it stored in one or more files or databases, and how are these interfaced?
Another important aspect in terms of automation is a complete overview of all steps in the data processing. Often, large amounts of data are processed in spreadsheets, sometimes with complex calculations. The more precisely manual steps and logics are known and analyzed, the higher the efficiency, quality and time savings will be.
Beyond automation, visualisation can help data tell a story that contributes to a better understanding of the compliance process as well as improved decision-making in strategic planning.
The typical benefits for the automation of intercompany transactions are:
- Accelerated processes: fast, automatic iterative processes and calculations, clear mapping
- Increased efficiency: no more time-consuming, manual data tasks and adjustments
- Reduced number of errors: higher data quality by implementing repeatable data quality checks including overview of findings
- Increased transparency: clear transparency of processes, applied rules and logics, and full traceability of processed data
- More meaningful insights: spend less time processing data and more time analyzing it with intuitive user interface
Data model structure
The typical data model structure is divided into the following five steps:
- Extract raw data from various sources
- Align and clean data
- Map raw data with clearly defined rules and exception handling
- Prepare, blend, parse, transform, test and adjust data
- Prepare output in required format for TPD
As soon as the data model has been set up, high efficiency gains can be realised, especially in the years following the implementation. 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.