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. The scope of work includes developing methods to extract meaningful information from tax documents and then researching how state-of-the-art machine learning methods can be combined to facilitate intelligent decision-making.

Faculty Supervisor:

Nathan Taback

Student:

Zain Nasrullah

Partner:

PricewaterhouseCoopers

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Toronto