Data analysis of thousands of invoices to support an arbitration
We used a factual and objective analysis of the data to counter an argument from the other party.
Our client, a financial institution, was involved in a dispute over uncollected VAT. The disputed matter concerned whether or not a company could have asked it’s customers to repay the full amount of VAT that the tax agency required the company to pay. The issue arose due to a period of time where the company did not collect the correct percentage of VAT from it’s customers.
The argument of the counterparty was that the number of requests was so large that the company would not have been practically capable of managing the repayment process. To evidence this, the counterparty filed the full VAT accountings of the Target from the previous five years, which proved to be well over 550,000 rows of data on Excel.
The data set was too large for the legal team to analyse themselves and there were strict deadlines imposed by the arbitration timeline. Outsourcing this task to another firm would have been expensive and challenging in the short time available. If we were unable to analyse the data, our client couldn’t have effectively challenged the counterparty’s position.
By aggregating and modelling the data using Power BI and a Python script, our Transform team were able to merge multiple spreadsheets into a single data set to analyse and interrogate. When carrying out the data analysis, our legal team were on-hand to validate the method and results of the data analysis. We then provided the results of the analysis in a simple, summarised format to our client’s expertise witness to support their argument.
Results and benefits
- The client was able to use the analysis to make an informed decision about whether to challenge the counterparty’s position.
- The client had access to our combined data analytics and legal services capabilities, which meant this could be carried out both more effectively and efficiently than engaging a forensic accounting firm separately.