Prediction of Optimal Business Structure for Tax Efficiency

In the Corporate Tax domain, professionals must review hundreds of documents in the process of filing taxes and rely on experience to identify where benefits or deductibles can be applied. This manual task is subject to human error and can result in unnecessary administrative overhead. With access to PricewaterhouseCoopers’ wealth of tax data, it is possible to develop a tool that uses historical trends to automate this process.

Automated transaction classification using machine learning algorithm

The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend.

Leveraging data analytics in modern tax function

Investigating geographical footprints of income shifting by multinational enterprises. PwC owns a large data set across all industries in Canada from its tax consulting engagements and annual standard tax filings from clients. This growing data source is an opportunity for accurate tax benchmarking, trend analysis and gaining deeper insights by transforming them into market differentiating knowledge that can be dynamically shared and accessed by multiple teams.